Numerical Modelling Methods for Ultrasonic Wave Propagation Through Polycrystalline Materials

Shivaprasad, S. M.; Krishnamurthy, C. V.; Pandala, Abhishek; Saini, Anuraag; Ramachandran, A.; Balasubramaniam, Krishnan

doi:10.1007/s12666-019-01739-4

Cited by 9 publications

(6 citation statements)

References 65 publications

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“…While modeling polycrystalline materials, each crystal is assigned a separate material property; hence, the number of boundary conditions increases. Therefore, numerical methods to model heat diffusion in a polycrystalline material are computationally expensive and time-consuming [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Data-driven simulation-assisted-Physics learned AI (DPAI) for heat diffusion in large grain polycrystalline materials

Bhemani,

Gantala,

Balasubramaniam

2024

Phys. Scr.

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In this paper, we propose a deep-learning algorithm, Data-driven simulation assisted Physics-learned Artificial Intelligence (DPAI), to simulate heat diffusion inlarge-grain polycrystalline material. The DPAI model utilizes an encoder-decoderarchitecture with convolutional long short-term memory (ConvLSTM), which capturesthe spatio-temporal representation from input sequences. The DPAI model learns thephysics of heat diffusion in the material from training datasets. This model is trainedwith Finite Element (FE) simulation datasets consisting of a varying number of grainsin a microstructure of polycrystalline material with a single-point heat source at thecenter. The arbitrary plane of the 3D microstructure of these materials is representedusing 2D Voronoi tessellations. These configurations are used to model the geometryof the 2D Computer-Aided Design (CAD) model. Each cell of the Voronoi tessellationrepresents one grain of the microstructure. This CAD model is used as the input tothe FE solver for solving heat diffusion equations. To model the near-realistic materialanisotropy and accurately measure temperature differences at cell boundaries, a smallermesh size is required in FE modeling, which takes considerable solver time. Therefore,the proposed model significantly reduces the computational time while maintainingreasonable accuracy compared to conventional FE simulation. The effectiveness ofthe trained DPAI model is examined by modeling larger domain problems involving agreater number of grains and varying material properties.

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Section: Introductionmentioning

confidence: 99%

Data-driven simulation-assisted-Physics learned AI (DPAI) for heat diffusion in large grain polycrystalline materials

Bhemani,

Gantala,

Balasubramaniam

2024

Phys. Scr.

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“…Efforts to model the ultrasonic wave propagation in materials with a microstructure have mainly focused on calculating attenuation and wave velocities in 3D, but backscattering has also been considered. Increasing computational power and the development of sophisticated computational methods such as finite difference (FD) or finite-difference time-domain (FDTD) methods [33,34] and finite element methods (FEM) [24,[35][36][37], have enabled the modelling and computation of large three-dimensional samples. In these studies, materials with (poly)crystalline structures, consisting of different phases, or with inclusions, voids or cracks have been modelled.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Grain Size and Composition in Steel Using Laser UltraSonics Simulations at Different Temperatures

Duijster

Volker²,

Berg³

et al. 2023

Applied Sciences

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The applicability of laser ultrasonics for the determination of grain size and phase composition in steels under different temperatures was investigated. This was done by obtaining the velocity and attenuation of propagating ultrasonic waves in a simulated steel medium. Samples of ferrite and austenite with varying microstructures were modelled and simulated with the finite difference method, as were samples with varying ratios of austenite and martensite. The temperature of the medium was taken into account as an essential parameter, since both velocity and attenuation are temperature dependent. Results of the velocity and attenuation analysis showed that the use of the wave propagation velocity is not feasible for determination of grain size or phase composition due to a high sensitivity to temperature and sample thickness. The frequency-dependent ultrasonic wave attenuation was less sensitive to the variation of temperature and sample thickness. It can be concluded that accurate knowledge of the temperature is essential for obtaining a correct grain size or phase ratio estimation: a temperature accuracy of 100 °C yields a grain size accuracy in the order of a micrometer using the attenuation. Similarly, a temperature accuracy of 70 °C leads to a phase ratio estimation accuracy of 10%.

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“…In recent years, various numerical methods based on elastodynamics theory have been used to study wave propagation and scattering. These methods include the Finite Element Method (FEM) [16,17], Finite Difference Method (FDM) [18][19][20], Finite Volume Method (FVM) (also known as Finite Integration Technique (FIT) [21]), and Boundary Element Method (BEM) [9,22]. Studies have shown that ultrasonic scattering or attenuation can be used to characterize poly-crystalline micro-structures and to localize flaws [17,23,24].…”

Section: Introductionmentioning

confidence: 99%

“…Establishing the relationship between ultrasonic scattering/attenuation and material parameters enables reliable and high-quality material testing [25,26]. The FEM has been used to model wave-material interactions in poly-crystalline materials [11,20,[25][26][27]. However, FEM presents limitations.…”

Section: Introductionmentioning

confidence: 99%

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Laplace Domain Boundary Element Method for Structural Health Monitoring of Poly-Crystalline Materials at Micro-Scale

Marrazzo,

Sharif Khodaei,

Aliabadi

2023

Applied Sciences

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This paper describes, for the first time, the application of an Elastodynamic Boundary Element Method (BEM) in Laplace Domain for the Structural Health Monitoring (SHM) of poly-crystalline materials. The study focuses on Ultrasonic Guided Wave (UGW) propagation and investigates the wave–material interactions at micro-scale. The study aims to investigate the interaction of UGWs with assessing micro-structural features such as grain size, morphology, degradation, and flaws. Numerical simulations of the most common micro-structural features demonstrate the accuracy and validity of the proposed method. Particular attention is paid to the study of porosity and its influence on material macro-properties. Different crystal morphologies such as cubic, rhombic, and truncated octahedral are considered. The detection of voids based on the changes in the amplitude and Time of Arrival (ToA) of the backscattered signal is investigated. The study also considers inter-granular cracks, which cause laceration, and examines flaw position/orientation, length, and distance from a specific reference. Furthermore, a framework is proposed for generating Probability of Detection (PoD) curves using numerical simulations. Experimental tests in pristine conditions are shown to be in good agreement with the numerical simulations in terms of ToA, signal amplitude, and wave velocity. The numerical simulations provide insights into wave propagation and wave–material interactions, including different types of defects at the micro-scale. Overall, the BEM and UGW methods are shown to be effective tools for better understanding micro-structural features and their influence on the macro-structural properties of poly-crystalline materials.

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Numerical Modelling Methods for Ultrasonic Wave Propagation Through Polycrystalline Materials

Cited by 9 publications

References 65 publications

Data-driven simulation-assisted-Physics learned AI (DPAI) for heat diffusion in large grain polycrystalline materials

Data-driven simulation-assisted-Physics learned AI (DPAI) for heat diffusion in large grain polycrystalline materials

Estimation of Grain Size and Composition in Steel Using Laser UltraSonics Simulations at Different Temperatures

Laplace Domain Boundary Element Method for Structural Health Monitoring of Poly-Crystalline Materials at Micro-Scale

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