More Like this

1. Modeling of rock inhomogeneity and anisotropy by explicit and implicit representation of microcracks

   Abedi, Reza ; Clarke, Philip L. ( January 2018 , Proceedings of ARMA 2018)

   Fracture in rock as a heterogeneous brittle material, having significant inherent randomness, requires including probabilistic considerations at different scales. Crack growth in rocks is generally associated with complex features such as crack path oscillations, microcrack and crack branching events. Two methods will be presented to address rock inhomogeneity and anisotropy. First, microcracks are explicitly realized in a domain based on specific statistics of crack length and location. Second, a statistical model is used to implicitly represent an inhomogeneous field for fracture strength. Both approaches can be used for rocks in which the natural fractures are oriented in a specific angle, i.e. an aspect for modeling bedding planes in sedimentary rocks. 

   more » « less
2. Tough and Ductile Architected Nacre‐Like Cementitious Composites

   https://doi.org/10.1002/adfm.202313516  

   Gupta, Shashank ; Esmaeeli, Hadi_S ; Moini, Reza ( June 2024 , Advanced Functional Materials)

   Enhancing fracture toughness and ductility of brittle materials such as concrete remains a challenge. Nature offers numerous mechanisms to enhance fracture toughness using purposeful designs of materials’ architecture. Natural nacre exhibits high fracture toughness by promoting inelastic deformation and hierarchical toughening mechanisms. Here, “nacre‐like‐separated” and “nacre‐like‐grooved” cementitious composites inspired by brick‐and‐mortar arrangement of mollusk shells are proposed. These nacre‐like composites are engineered by laser processing cement paste into individual tablets and grooved patterns (as intentional defects) and laminating them with limited amounts of suitable elastomeric (polyvinyl siloxane) interlayers. It is found that interlayer deformation, tortuous crack propagation guided by the defects, and crack bridging are the main toughening mechanisms in these composites that lead to rising resistance curves. The study hypothesizes tablet sliding as an additional toughening mechanism in “nacre‐like‐separated”, preventing tablet failure and leading to the postponed onset of bulk composite failure. These mechanisms significantly enhance both fracture toughness and ductility by 17.1 and 19 folds, compared to constituent hardened cement paste, respectively. By engineering laser‐induced defects into tabulated cementitious‐elastomeric material at meso‐scale, a class of tough and ductile cementitious composites is introduced, resulting in significantly high fracture toughness values (73.68 MPa.mm^1/2), comparable to Ultra‐high‐performance‐concrete without sacrificing the strength.

    

   more » « less
3. Comparison of Interfacial and Continuum Models for Dynamic Fragmentation Analysis

   https://doi.org/10.1115/IMECE2018-88294  

   Bahmani, Bahador ; Clarke, Philip ; Abedi, Reza ( November 2018 , Proceedings of ASME 2018 International Mechanical Engineering Congress and Exposition)

   The microstructural design has an essential effect on the fracture response of brittle materials. We present a stochastic bulk damage formulation to model dynamic brittle fracture. This model is compared with a similar interfacial model for homogeneous and heterogeneous materials. The damage models are rate-dependent, and the corresponding damage evolution includes delay effects. The delay effect provides mesh objectivity with much less computational efforts. A stochastic field is defined for material cohesion and fracture strength to involve microstructure effects in the proposed formulations. The statistical fields are constructed through the Karhunen-Loeve (KL) method. An advanced asynchronous Spacetime Discontinuous Galerkin (aSDG) method is used to discretize the final system of coupled equations. Application of the presented formulation is shown through dynamic fracture simulation of rock under a uniaxial compressive load. The final results show that a stochastic bulk damage model produces more realistic results in comparison with a homogenizes model.

    

   more » « less
4. Highly Stretchable and Tough Hydrogels below Water Freezing Temperature

   https://doi.org/10.1002/adma.201801541  

   Morelle, Xavier P. ; Illeperuma, Widusha R. ; Tian, Kevin ; Bai, Ruobing ; Suo, Zhigang ; Vlassak, Joost J. ( July 2018 , Advanced Materials)

   Hydrogels consist of hydrophilic polymer networks dispersed in water. Many applications of hydrogels rely on their unique combination of solid‐like mechanical behavior and water‐like transport properties. If the temperature is lowered below 0 °C, however, hydrogels freeze and become rigid, brittle, and non‐conductive. Here, a general class of hydrogels that do not freeze at temperatures far below 0 °C, while retaining high stretchability and fracture toughness, is demonstrated. These hydrogels are synthesized by adding a suitable amount of an ionic compound to the hydrogel. The present study focuses on tough polyacrylamide‐alginate double network hydrogels equilibrated with aqueous solutions of calcium chloride. The resulting hydrogels can be cooled to temperatures as low as −57 °C without freezing. In this temperature range, the hydrogels can still be stretched more than four times their initial length and have a fracture toughness of 5000 J m⁻². It is anticipated that this new class of hydrogels will prove useful in developing new applications operating under a broad range of environmental and atmospheric conditions.

    

   more » « less
5. A Generative Reinforcement Learning Framework for Predictive Analytics

   Skordilis, Erotokritos ; Moghaddass, Ramin. ; Farhat, Tazin. ( January 2023 , Proceedings annual Reliability and Maintainability Symposium)

   In this work, we present a new approach for latent system dynamics and remaining useful life (RUL) estimation of complex degrading systems using generative modeling and reinforcement learning. The main contributions of the proposed method are two-fold. First, we show how a deep generative model can approximate the functionality of high-fidelity simulators and, thus, is able to substitute expensive and complex physics-based models with data-driven surrogate ones. In other words, we can use the generative model in lieu of the actual system as a surrogate model of the system. Furthermore, we show how to use such surrogate models for predictive analytics. Our method follows two main steps. First, we use a deep variational autoencoder (VAE) to learn the distribution over the latent state-space that characterizes the dynamics of the system under monitoring. After model training, the probabilistic VAE decoder becomes the surrogate system model. Then, we develop a scalable reinforcement learning framework using the decoder as the environment, to train an agent for identifying adequate approximate values of the latent dynamics, as well as the RUL.To our knowledge, the method presented in this paper is the first in industrial prognostics that utilizes generative models and reinforcement learning in that capacity. While the process requires extensive data preprocessing and environment tailored design, which is not always possible, it demonstrates the ability of generative models working in conjunction with reinforcement learning to provide proper value estimations for system dynamics and their RUL. To validate the quality of the proposed method, we conducted numerical experiments using the train_FD002 dataset provided by the NASA CMAPSS data repository. Different subsets were used to train the VAE and the RL agent, and a leftover set was then used for model validation. The results shown prove the merit of our method and will further assist us in developing a data-driven RL environment that incorporates more complex latent dynamic layers, such as normal/faulty operating conditions and hazard processes. 

   more » « less