4,905 research outputs found

    Genetic algorithm-based pore network extraction from micro-computed tomography images

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    A genetic-based pore network extraction method from micro-computed tomography (micro-CT) images is proposed in this paper. Several variables such as the number, radius and location of pores, the coordination number, as well as the radius and length of the throats are used herein as the optimization parameters. Two approaches to generate the pore network structure are presented. Unlike previous algorithms, the presented approaches are directly based on minimizing the error between the extracted network and the real porous medium. This leads to the generation of more accurate results while reducing required computational memories. Two different objective functions are used in building the network. In the first approach, only the difference between the real micro-CT images of the porous medium and the sliced images from the generated network is selected as the objective function which is minimized via a genetic algorithm (GA). In order to further improve the structure and behavior of the generated network, making it more representative of the real porous medium, a second optimization has been used in which the contrast between the experimental and the predicted values of the network permeability is minimized via GA. We present two case studies for two different complex geological porous media, Clashach sandstone and Indiana limestone. We compare porosity and permeability predicted by the GA generated networks with experimental values and find an excellent match

    Computational characterization and prediction of metal-organic framework properties

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    In this introductory review, we give an overview of the computational chemistry methods commonly used in the field of metal-organic frameworks (MOFs), to describe or predict the structures themselves and characterize their various properties, either at the quantum chemical level or through classical molecular simulation. We discuss the methods for the prediction of crystal structures, geometrical properties and large-scale screening of hypothetical MOFs, as well as their thermal and mechanical properties. A separate section deals with the simulation of adsorption of fluids and fluid mixtures in MOFs

    Structure and pressure drop of real and virtual metal wire meshes

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    An efficient mathematical model to virtually generate woven metal wire meshes is presented. The accuracy of this model is verified by the comparison of virtual structures with three-dimensional images of real meshes, which are produced via computer tomography. Virtual structures are generated for three types of metal wire meshes using only easy to measure parameters. For these geometries the velocity-dependent pressure drop is simulated and compared with measurements performed by the GKD - Gebr. Kufferath AG. The simulation results lie within the tolerances of the measurements. The generation of the structures and the numerical simulations were done at GKD using the Fraunhofer GeoDict software

    Development and Applications of Self-learning Simulation in Finite Element Analysis

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    Numerical analysis such as the finite element analysis (FEA) have been widely used to solve many engineering problems. Constitutive modelling is an important component of any numerical analysis and is used to describe the material behaviour. The accuracy and reliability of numerical analysis is greatly reliant on the constitutive model that is integrated in the finite element code. In recent years, data mining techniques such as artificial neural network (ANN), genetic programming (GP) and evolutionary polynomial regression (EPR) have been employed as alternative approach to the conventional constitutive modelling. In particular, EPR offers great advantages over other data mining techniques. However, these techniques require a large database to learn and extract the material behaviour. On the other hand, the link between laboratory or field tests and numerical analysis is still weak and more investigation is needed to improve the way that they matched each other. Training a data mining technique within the self-learning simulation framework is currently considered as one of the solutions that can be utilised to accurately represent the actual material behaviour. In this thesis an EPR based machine learning technique is utilised in the heart of the self-learning framework with an automation process which is coded in MATLAB environment. The methodology is applied to simulate different material behaviour in a number of structural and geotechnical applications. Two training strategies are used to train the EPR in the developed framework, total stress-strain and incremental stress-strain strategies. The results show that integrating EPR based models in the framework allows to learn the material response during the self-learning process and provide accurate predictions to the actual behaviour. Moreover, for the first time, the behaviour of a complex material, frozen soil, is modelled based on the EPR approach. The results of the EPR model predictions are compared with the actual data and it is shown that the proposed model can capture and reproduce the behaviour of the frozen soil with a very high accuracy. The developed EPR based self-learning methodology presents a unified approach to material modelling that can also help the user to gain a deeper insight into the behaviour of the materials. The methodology is generic and can be extended to modelling different engineering materials

    Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions

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    Artificial intelligence (AI) is rapidly emerging as an enabling tool for solving various complex materials design problems. This paper aims to review recent advances in AI-driven materials-by-design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro-morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials-by-design, namely representation learning of microstructure morphology (i.e., shape descriptors), structure-property-performance (S-P-P) linkage estimation, and optimization/design exploration. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materials-by-design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials-by-design, such as meta-learning, active learning, Bayesian learning, and semi-/weakly-supervised learning, to bridge the gap between machine learning research and EM research

    Investigating biocomplexity through the agent-based paradigm.

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    Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex

    Stochastic Parameter Estimation of Poroelastic Processes Using Geomechanical Measurements

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    Understanding the structure and material properties of hydrologic systems is important for a number of applications, including carbon dioxide injection for geological carbon storage or enhanced oil recovery, monitoring of hydraulic fracturing projects, mine dewatering, environmental remediation and managing geothermal reservoirs. These applications require a detailed knowledge of the geologic systems being impacted, in order to optimize their operation and safety. In order to evaluate, monitor and manage such hydrologic systems, a stochastic estimation framework was developed which is capable of characterizing the structure and physical parameters of the subsurface. This software framework uses a set of stochastic optimization algorithms to calibrate a heterogeneous subsurface flow model to available field data, and to construct an ensemble of models which represent the range of system states that would explain this data. Many of these systems, such as oil reservoirs, are deep and hydraulically isolted from the shallow subsurface making near-surface fluid pressure measurements uninformative. Near-surface strainmeter, tiltmeter and extensometer signals were therefore evaluated in terms of their potential information content for calibrating poroelastic flow models. Such geomechanical signals are caused by mechanical deformation, and therefore travel through hydraulically impermeable rock much more quickly. A numerical geomechanics model was therefore developed using Geocentric, which couples subsurface flow and elastic deformation equations to simulate geomechanical signals (e.g. pressure, strain, tilt and displacement) given a set of model parameters. A high-performance cluster computer performs this computationally expensive simulation for each set of parameters, and compares the simulation results to measured data in order to evaluate the likelihood of each model. The set of data-model comparisons are then used to estimate each unknown parameter, as well as the uncertainty of each parameter estimate. This uncertainty can be inuenced by limitations in the measured dataset such as random noise, instrument drift, and the number and location of sensors, as well as by conceptual model errors and false underlying assumptions. In this study we find that strain measurements taken from the shallow subsurface can be used to estimate the structure and material parameters of geologic layers much deeper in the subsurface. This can signicantly mitigate drilling and installation costs of monitoring wells, as well as reduce the risk of puncturing or fracturing a target reservoir. These parameter estimates were also used to develop an ensemble of calibrated hydromechanical models which can predict the range of system behavior and inform decision-making on the management of an aquifer or reservoir

    Simulation and Analysis of Unconventional Reservoirs Using Fast Marching Method and Transient Drainage Volume

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    Unconventional tight/shale reservoirs have become an important component of the world’s energy map in the recent decade and have been attracting a lot of interests in both academia and industry. However, the industry today still faces significant challenges in understanding the fundamental mechanisms. Unconventional tight/shale reservoirs are characterized by low or ultra-low permeability, such that the transient pressure behavior might last throughout the production lifetime. Recent research has proposed a novel approach for unconventional reservoir analysis based on the high-frequency asymptotic approximation of diffusivity equation. By solving the Eikonal equation with the Fast Marching Method (FMM), one can rapidly obtain the diffusive time of flight (DToF) which depicts the pressure transient propagation process. A fast DToF-based forward simulation is further proposed to solve the fluid flow equation in a 1D equivalent coordinate system, with the DToF as the spatial coordinate. In this study, we first adopt the DToF-based simulation as a rapid forward simulator to formulate an efficient hydraulic fracture design and optimization workflow. The DToF-based simulation can be orders of magnitude faster than the conventional finite difference/volume based simulation, and is ideal for optimization process where hundreds or thousands of simulations are necessary. Our workflow focuses on optimizing the number of hydraulic fracture stages, their spacing, and the allocation of proppant. The workflow also accounts for the geologic uncertainty, which given by different natural fracture distributions. Next, we extend this DToF-based simulation from Cartesian and corner point grid system to unstructured grids to better characterize the complex fracture geometry induced by hydraulic fracturing job. Two different constructions of the local Eikonal equation solver, based on Fermat’s principle and Eulerian discretization, are investigated and compared. Numerical examples are presented to illustrate the power and validity of this extended DToF-based simulation workflow. Finally, we propose a model-free production data analysis method to analyze the performance of unconventional reservoirs when a full simulation model is not available. The transient drainage volume is derived directly based on bottom-hole pressure and production rate. We further define the drainage volume derivative and instantaneous recovery ratio, which can measure how effectively the hydraulic fractures have stimulated the reservoir. This technique is then applied to select candidate wells for refracturing

    A simple and effective geometric representation for irregular porous structure modeling

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    Computer-aided design of porous structures is a challenging task because of the high degree of irregularity and intricacy associated with the geometries. Most of the existing design approaches either target designing artifacts with regular-shaped pores or reconstructing geometric models from existing porous objects. For regular porous structures, it is difficult to control the pore shapes and distributions locally; for reconstructed models, a design is attainable only if there are some existing objects to reconstruct from. This paper is motivated to present an alternative approach to design irregular porous artifacts with controllable pore shapes and distributions, yet without requiring any existing objects as prerequisites. Inspired by the random colloid-aggregation model which explains the formation mechanism of random porous media, Voronoi tessellation is first generated to partition the space into a collection of compartments. Selective compartments are then merged together to imitate the random colloid aggregations. Through this Voronoi cell merging, irregular convex and concave polygons are obtained and the vertices of which are modeled as control points of closed B-Spline curves. The fitted B-Spline curves are then employed to represent the boundaries of the irregular-shaped pores. The proposed approach drastically improved the ease of irregular porous structure modeling while at the same time properly maintained the irregularity that is widely found in natural objects. Compared with other existing CAD approaches, the proposed approach can easily construct irregular porous structures which appear more natural and realistic. © 2010 Elsevier Ltd. All rights reserved.postprin
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