arXiv subjects

Carlo Janna

Publications and source records attributed to Carlo Janna.

8 recordsLinked to original sources

arXiv2026

Unexpected fault activation due to underground gas storage in produced reservoirs. Part I: Mathematical model and mechanisms

Underground gas storage (UGS) is a critical technology for managing seasonal gas consumption peaks, increasingly important in the face of market uncertainties. However, safety concerns arise when reactivating pre-existing faults in faulted basins, where human activities may trigger seismic events. Typically, faults are reactivated when shear stress exceeds a critical frictional threshold, but unexpected fault reactivations have been observed during cushion gas injection (CGI) and UGS cycles in the Netherlands, even when the stress regime suggests stability. This two-part study introduces a novel simulation framework to better understand the mechanisms behind fault reactivation in complex settings such as the Rotliegend formation in the Netherlands. A 3D mathematical model coupling frictional contact mechanics in faulted porous rocks with fluid flow allows for predictive analysis of fault behavior. The effect of the storage of different fluids for various purposes, such as the long-term sequestration of CO2, the regular injection and extraction cycles of CH4, and the highly irregular cycles of H2, is investigated with respect to fault activation hazard. The ultimate goal is to define a safe operational bandwidth for UGS activities in faulted reservoirs. Part I of this work presents this comprehensive simulation tool where a slip-weakening constitutive law is introduced to model fault behavior. The approach is designed to address the complex geological setting that characterizes the Rotliegend formation, where multiple factors influence the behavior of fault systems. We succeed in explaining and modeling the occurrence of unexpected fault reactivations. The analysis shows that reactivation during primary production (PP) causes stress redistribution, leading to a new deformed equilibrated configuration.

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arXiv2026

Unexpected fault activation due to underground gas storage in produced reservoirs. Part II: Definition of safe operational bandwidths

Underground gas storage is a versatile tool for managing energy resources and addressing pressing environmental concerns. While natural gas is stored in geological formations since the early 20th century, hydrogen has recently been considered as a potential candidate toward a more flexible and sustainable energy infrastructure. Furthermore, these formations can additionally capture gases that contribute to climate change, such as CO2. When such operations are implemented in faulted basins, however, safety concerns may arise due to the potential reactivation of pre-existing faults, which could trigger (micro)-seismicity events. In the Netherlands, it has been recently noted that fault reactivation can occur "unexpectedly" during the life of an underground gas storage (UGS) site, even when stress conditions are not expected to cause a failure. The present two-part work aims to develop a modeling framework to investigate the physical mechanisms causing such occurrences in previously produced gas reservoirs and define a safe operational bandwidth for pore pressure variation for UGS operations in the faulted reservoirs of the Upper Rotliegend Group, the Netherlands. This paper investigates in detail the mechanisms and crucial factors that result in fault reactivation at various stages of a UGS. The mathematical and numerical model described in Part I is used, also considering how the presence of stored gases may influence the mechanical properties of the reservoir and caprock, in particular the Young modulus. The study investigates the hazard of fault activation caused by the storage of different fluids for various purposes, such as long-term CO2 sequestration, CH4 and H2 injection and extraction cycles, and N2 injection as cushion gas. The results show how geological configuration, geomechanical properties, and reservoir operating conditions may increase the hazard of fault reactivation.

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arXiv2024

A multigrid reduction framework for domains with symmetries

Divergence constraints are present in the governing equations of numerous physical phenomena, and they usually lead to a Poisson equation whose solution represents a bottleneck in many simulation codes. Algebraic Multigrid (AMG) is arguably the most powerful preconditioner for Poisson's equation, and its effectiveness results from the complementary roles played by the smoother, responsible for damping high-frequency error components, and the coarse-grid correction, which in turn reduces low-frequency modes. This work presents several strategies to make AMG more compute-intensive by leveraging reflection, translational and rotational symmetries. AMGR, our final proposal, does not require boundary conditions to be symmetric, therefore applying to a broad range of academic and industrial configurations. It is based on a multigrid reduction framework that introduces an aggressive coarsening to the multigrid hierarchy, reducing the memory footprint, setup and application costs of the top-level smoother. While preserving AMG's excellent convergence, AMGR allows replacing the standard sparse matrix-vector product with the more compute-intensive sparse matrix-matrix product, yielding significant accelerations. Numerical experiments on industrial CFD applications demonstrated up to 70% speed-ups when solving Poisson's equation with AMGR instead of AMG. Additionally, strong and weak scalability analyses revealed no significant degradation.

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arXiv2022

Parallel Energy-Minimization Prolongation for Algebraic Multigrid

Algebraic multigrid (AMG) is one of the most widely used solution techniques for linear systems of equations arising from discretized partial differential equations. The popularity of AMG stems from its potential to solve linear systems in almost linear time, that is with an O(n) complexity, where n is the problem size. This capability is crucial at the present, where the increasing availability of massive HPC platforms pushes for the solution of very large problems. The key for a rapidly converging AMG method is a good interplay between the smoother and the coarse-grid correction, which in turn requires the use of an effective prolongation. From a theoretical viewpoint, the prolongation must accurately represent near kernel components and, at the same time, be bounded in the energy norm. For challenging problems, however, ensuring both these requirements is not easy and is exactly the goal of this work. We propose a constrained minimization procedure aimed at reducing prolongation energy while preserving the near kernel components in the span of interpolation. The proposed algorithm is based on previous energy minimization approaches utilizing a preconditioned restricted conjugate gradients method, but has new features and a specific focus on parallel performance and implementation. It is shown that the resulting solver, when used for large real-world problems from various application fields, exhibits excellent convergence rates and scalability and outperforms at least some more traditional AMG approaches.

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arXiv2021

A Reverse Augmented Constraint preconditioner for Lagrange multiplier methods in contact mechanics

Frictional contact is one of the most challenging problems in computational mechanics. Typically, it is a tough nonlinear problem often requiring several Newton iterations to converge and causing troubles also in the solution to the related linear systems. When contact is modeled with the aid of Lagrange multipliers, the impenetrability condition is enforced exactly, but the associated Jacobian matrix is indefinite and needs a special treatment for a fast numerical solution. In this work, a constraint preconditioner is proposed where the primal Schur complement is computed after augmenting the zero block. The name Reverse is used in contrast to the traditional approach where only the structural block undergoes an augmentation. Besides being able to address problems characterized by singular structural blocks, often arising in contact mechanics, it is shown that the proposed approach is significantly cheaper than traditional constraint preconditioning for this class of problems and it is suitable for an efficient HPC implementation through the Chronos parallel package. Our conclusions are supported by several numerical experiments on mid- and large-size problems from various applications. The source files implementing the proposed algorithm are freely available on GitHub.

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arXiv2021

Chronos: A general purpose classical AMG solver for High Performance Computing

The numerical simulation of the physical systems has become in recent years a fundamental tool to perform analyses and predictions in several application fields, spanning from industry to the academy. As far as large scale simulations are concerned, one of the most computationally expensive task is the solution of linear systems arising from the discretization of the partial differential equations governing the physical processes.This work presents Chronos, a collection of linear algebra functions specifically designed for the solution of large, sparse linear systems on massively parallel computers (https://www.m3eweb.it/chronos/). Its emphasis is on modern, effective and scalable AMG preconditioners for High Performance Computing (HPC). This work describes the numerical algorithms and the main structures of this software suite, especially from the implementation standpoint. Several numerical results arising from practical mechanics and fluid dynamics applications with hundreds of millions of unknowns are addressed and compared with other state-of-the-art linear solvers, proving Chronos efficiency and robustness.

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arXiv2020

A GPU-accelerated adaptive FSAI preconditioner for massively parallel simulations

The solution of linear systems of equations is a central task in a number of scientific and engineering applications. In many cases the solution of linear systems may take most of the simulation time thus representing a major bottleneck in the further development of scientific and technical software. For large scale simulations, nowadays accounting for several millions or even billions of unknowns, it is quite common to resort to preconditioned iterative solvers for exploiting their low memory requirements and, at least potential, parallelism. Approximate inverses have been shown to be robust and effective preconditioners in various contexts. In this work, we show how adaptive FSAI, an approximate inverse characterized by a very high degree of parallelism, can be successfully implemented on a distributed memory computer equipped with GPU accelerators. Taking advantage of GPUs in adaptive FSAI set-up is not a trivial task, nevertheless we show through an extensive numerical experimentation how the proposed approach outperforms more traditional preconditioners and results in a close-to-ideal behaviour in challenging linear algebra problems.

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arXiv2019

A robust adaptive algebraic multigrid linear solver for structural mechanics

The numerical simulation of structural mechanics applications via finite elements usually requires the solution of large-size and ill-conditioned linear systems, especially when accurate results are sought for derived variables interpolated with lower order functions, like stress or deformation fields. Such task represents the most time-consuming kernel in commercial simulators; thus, it is of significant interest the development of robust and efficient linear solvers for such applications. In this context, direct solvers, which are based on LU factorization techniques, are often used due to their robustness and easy setup; however, they can reach only superlinear complexity, in the best case, thus, have limited applicability depending on the problem size. On the other hand, iterative solvers based on algebraic multigrid (AMG) preconditioners can reach up to linear complexity for sufficiently regular problems but do not always converge and require more knowledge from the user for an efficient setup. In this work, we present an adaptive AMG method specifically designed to improve its usability and efficiency in the solution of structural problems. We show numerical results for several practical applications with millions of unknowns and compare our method with two state-of-the-art linear solvers proving its efficiency and robustness.

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