Modification and Application of Limestone HJC Constitutive Model under the Impact Load

Tian, Xingchao; Tao, Tiejun; Lou, Qianxing; Xie, Caijin

doi:10.2113/2022/6443087

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…For rock materials, the values of 𝐾 , 𝐾 , and 𝐾 come from the test data of Larson et al [21] and are fitted via 𝑃 = 𝐾 𝜇̅ + 𝐾 𝜇̅ + 𝐾 𝜇̅ to obtain 𝐾 = 43 GPa, 𝐾 = -257 GPa, and 𝐾 = 596 GPa. For the value of 𝑃 , we refer to the research results of Tian et al [22]; this is an insensitive parameter, and 𝑃 = 1.035 GPa is taken herein. The HJC model parameters for the sandstone samples are given in Table 3.…”

Section: Parameters Of Sandstone Holmquist-johnson-cook Modelmentioning

confidence: 99%

Dynamic Brazilian tests of yellow sandstone under coupled static and dynamic loads

Zhang,

Xu,

et al. 2024

J. vibroeng.

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Through a series of static mechanical tests and (SHPB) dynamic impact tests, the static and dynamic mechanical parameters of rock as represented by yellow sandstone are determined, and the Holmquist-Johnson-Cook model parameters of the rock are calibrated using the test data and theoretical calculations. The feasibility of a numerical model is verified, and numerical analysis of the SHPB impact process under different radial pressure pre-loading is carried out on the basis of good verification. The results show that with increasing impact load, the degree of rock breakage increases, as does the dynamic tensile strength. With the application of increasing pre-static pressure, the dynamic tensile strength of the rock decreases gradually, and the maximum radial cumulative strain increases continuously under a given impact pressure, indicating that micro-cracks in the rock develop initially and then expand under the influence of pre-static pressure; the rock is more easily broken, and its weakening degree increases. Under coupled dynamic and static loading, the energy utilization rate of rock in the Brazilian splitting process is jointly affected by axial compression ratio and impact load. Too large a pressure ratio will reduce the strain-rate sensitivity of rock, resulting in low energy utilization rate, while too low an axial compression ratio will make the dynamic tensile strength of rock relatively high, which is not conducive to tensile failure. Therefore, on the premise of clear fracture form requirements, a suitable combination of axial compression ratio and impact velocity can improve the rock crushing effect and energy utilization rate.

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Section: Parameters Of Sandstone Holmquist-johnson-cook Modelmentioning

confidence: 99%

Dynamic Brazilian tests of yellow sandstone under coupled static and dynamic loads

Zhang,

Xu,

et al. 2024

J. vibroeng.

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“…257 GPa, and K 3 = 596 GPa. For the value of P lock , we refer to the research results of (Tian et al, 2022); this is an insensitive parameter, and P lock = 1.035 GPa is taken herein. The HJC model parameters for the sandstone samples are given in Table 5.…”

Section: Parameters Of Sandstone Hjc Modelmentioning

confidence: 99%

Numerical simulation on dynamic compression properties of sandstone under axial static preload

Xu,

Zhang,

et al. 2024

Front. Mater.

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In this study, through a series of static mechanical tests and split Hopkinson pressure bar (SHPB) dynamic impact tests, the static and dynamic mechanical parameters of yellow sandstone are determined, and the Holmquist–Johnson–Cook model parameters of the rock are determined by the test data and theoretical calculation. The feasibility of a numerical model is verified, based on which the SHPB impact process under different axial pressure is subjected to numerical analysis. The results show that with increasing impact load, the degree of rock breakage increases, and the dynamic compressive strength and dynamic elastic modulus increase continuously. With the application and increase of axial pressure, the dynamic compressive strength and dynamic elastic modulus of the rock decrease gradually under the same impact load, and the maximum cumulative strain keeps increasing, indicating that under the influence of axial pressure, micro-cracks in the rock have initially developed and expanded. With increasing axial pressure, the rock is more vulnerable to breakage, and its weakening degree keeps increasing. The energy utilization rate of one-dimensional dynamic and static combined loading is affected by the axial compression ratio and impact load. At low axial compression ratio, the rock has high impact resistance but high energy utilization rate; at high axial compression ratio, the rock has low impact resistance but low energy utilization rate. Therefore, the combination of axial compression ratio and impact velocity can improve the crushing effect and energy utilization rate on the premise of clear crushing form requirements.

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“…The HJC model basically consists of three aspects: a strength equation, an equation of state, and a damage evolution equation. This study aims to reproduce the research findings of several papers [44][45][46][47][48] and to compare them with actual drilling results to determine the constitutive model parameters for limestone, granite, and sandstone (Tables 7-9). In these tables, RO is the mass density, G is the shear modulus, A is the normalized cohesive strength, B is the normalized pressure hardening, C is the strain rate coefficient, N is the pressure hardening exponent, fc is the uniaxial compressive strength, T is the maximum hydrostatic pressure, EPS0 stands for the maximum tensile hydrostatic pressure, S fmax is the normalized maximum strength, P c is the crushing pressure, µc is the crushing volumetric strain, PL is the locking pressure, µL is the locking volumetric strain, D 1 and D 2 are the damage constants, K 1 -K 3 are the pressure constants, and FS is the failure type.…”

Section: Dof †mentioning

confidence: 99%

Dynamic Numerical Simulation and Transfer Learning-Based Rapid Rock Identification during Measurement While Drilling (MWD)

Fang,

Wu,

Jiang

et al. 2024

Processes

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In constructing rapid rock identification models for measurement while drilling (MWD) via neural network methods, collecting actual drilling data to train the model is extremely time-consuming and labor-intensive. This requires extensive drilling experiments in various rock types, resulting in limited neural network training data for rock identification that covers a limited range of rock types. To suitably address this issue, a dynamic numerical simulation model for rock drilling is established that generates extensive drilling data. The input parameters for the simulations include torque, drill bit rotation speed, and drilling speed. A neural network model is then developed for rock classification using large datasets from dynamic numerical simulations, specifically those of granite, limestone, and sandstone. Building upon this model, transfer learning is appropriately applied to store the knowledge obtained in the rock identification based on the neural network model. Further training through transfer learning is conducted with smaller datasets obtained during actual drilling, making the model suitable for practical rock identification and prediction in the drilling processes. The neural network rock classification model, incorporating dynamic numerical simulation and transfer learning, achieves a prediction accuracy of 99.36% for granite, 99.53% for sandstone, and 99.82% for limestone. This reveals an enhancement in prediction accuracy of up to 22.94% compared to the models without transfer learning.

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Modification and Application of Limestone HJC Constitutive Model under the Impact Load

Cited by 11 publications

References 5 publications

Dynamic Brazilian tests of yellow sandstone under coupled static and dynamic loads

Dynamic Brazilian tests of yellow sandstone under coupled static and dynamic loads

Numerical simulation on dynamic compression properties of sandstone under axial static preload

Dynamic Numerical Simulation and Transfer Learning-Based Rapid Rock Identification during Measurement While Drilling (MWD)

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