arXiv subjects

Chandrasekhar Annavarapu

Publications and source records attributed to Chandrasekhar Annavarapu.

4 recordsLinked to original sources

arXiv2026

Phase-field analysis of fracture in heterogeneous wellbore systems: effects of casing eccentricity and cement-formation interface strength

Predicting the initiation and propagation of cracks in heterogeneous wellbore systems under complex in-situ conditions remains challenging. We present a hybrid phase-field fracture framework to model crack growth in heterogeneous wellbore systems with weak interfaces. The framework is first validated against benchmark problems with available analytical and numerical solutions. Subsequently, numerical experiments are conducted to isolate the effects of interface strength and casing eccentricity on crack growth. The results show that casing eccentricity strongly influences both the pressure at crack initiation and the resulting crack paths, reducing the crack initiation pressure by up to 30% relative to the concentric configuration. For the examples considered here, beyond a critical eccentricity threshold of 50%, localized shear stresses drive the nucleation of inclined cracks in the rock formation in addition to radial cracking -- a failure mode absent in concentric configurations. For sufficiently weak interfaces (i.e., interfaces with 30% of the toughness of the surrounding bulk material), radially propagating cracks in the cement sheath are deflected along the interface rather than penetrating into the rock formation. This deflection delays stress relaxation within the sheath, promotes the nucleation of additional radial cracks, and increases the risk of sustained casing pressure and wellbore failure. Finally, a three-dimensional simulation reveals depth-dependent crack nucleation, stress-shadow effects that suppress full-depth crack growth, and crack coalescence along the cement-rock interface -- phenomena that are fundamentally inaccessible under plane-strain assumptions -- demonstrating the applicability of the framework to realistic heterogeneous wellbore systems.

cs.CE↗

arXiv2024

Physics-informed Neural Networks for Heterogeneous Poroelastic Media

This study presents a novel physics-informed neural network (PINN) framework for modeling poroelasticity in heterogeneous media with material interfaces. The approach introduces a composite neural network (CoNN) where separate neural networks predict displacement and pressure variables for each material. While sharing identical activation functions, these networks are independently trained for all other parameters. To address challenges posed by heterogeneous material interfaces, the CoNN is integrated with the Interface-PINNs or I-PINNs framework (Sarma et al. 2024, https://dx.doi.org/10.1016/j.cma.2024.117135), allowing different activation functions across material interfaces. This ensures accurate approximation of discontinuous solution fields and gradients. Performance and accuracy of this combined architecture were evaluated against the conventional PINNs approach, a single neural network (SNN) architecture, and the eXtended PINNs (XPINNs) framework through two one-dimensional benchmark examples with discontinuous material properties. The results show that the proposed CoNN with I-PINNs architecture achieves an RMSE that is two orders of magnitude better than the conventional PINNs approach and is at least 40 times faster than the SNN framework. Compared to XPINNs, the proposed method achieves an RMSE at least one order of magnitude better and is 40% faster.

eess.SY↗

arXiv2024

Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-informed Neural Networks Framework for Interface Problems

We present an efficient physics-informed neural networks (PINNs) framework, termed Adaptive Interface-PINNs (AdaI-PINNs), to improve the modeling of interface problems with discontinuous coefficients and/or interfacial jumps. This framework is an enhanced version of its predecessor, Interface PINNs or I-PINNs (Sarma et al.; https://dx.doi.org/10.2139/ssrn.4766623), which involves domain decomposition and assignment of different predefined activation functions to the neural networks in each subdomain across a sharp interface, while keeping all other parameters of the neural networks identical. In AdaI-PINNs, the activation functions vary solely in their slopes, which are trained along with the other parameters of the neural networks. This makes the AdaI-PINNs framework fully automated without requiring preset activation functions. Comparative studies on one-dimensional, two-dimensional, and three-dimensional benchmark elliptic interface problems reveal that AdaI-PINNs outperform I-PINNs, reducing computational costs by 2-6 times while producing similar or better accuracy.

cs.LG↗

arXiv2020

A stabilized finite element method for delamination analysis of composites using cohesive elements

We demonstrate the ability of a stabilized finite element method, inspired by the weighted Nitsche approach, to alleviate spurious traction oscillations at interlaminar interfaces in multi-ply multi-directional composite laminates. In contrast with the standard (penalty-like) method, the stabilized method allows the use of arbitrarily large values of cohesive stiffness and obviates the need for engineering approaches to estimate minimum cohesive stiffness necessary for accurate delamination analysis. This is achieved by defining a weighted interface traction in the stabilized method, which allows a gradual transition from penalty-like method for soft elastic contact to Nitsche-like method for rigid contact. We conducted several simulation studies involving constant strain patch tests and benchmark delamination tests under mode-I, mode-II and mixed-mode loadings. Our results show clear evidence of traction oscillations with the standard method with structured and perturbed finite element meshes, and that the stabilized method alleviates these oscillations, thus illustrating its robustness.

cs.CE↗