Scipedia
Not a member yet
33380 research outputs found
Sort by
AI-accelerated computational multiscale modeling
No full textMultiscale modeling is the branch of computational mechanics that has suffered from the limits imposed by computational costs probably the most, owing to computations being performed on more than one scale. Consequently, the use of machine learning (ML) methods to bypass costly lower-scale or scale-bridging simulations has tremendous potential to accelerate multiscale modeling and provide new opportunities for simulating the complex mechanics of materials and structures. We will discuss a few recent applications of ML to the field of computational mechanics, all united by the ambition to accelerate simulations across scales. One example is ML-based surrogate material modeling to bypass expensive material point calculations, e.g., by using Recurrent Neural Operators (RNOs) as surrogates for computationally expensive constitutive laws. This is demonstrated for finite-strain crystal plasticity models for magnesium, which are typically too expensive to be used in a multiscale setting. Similarly, FE2 simulations can be considerably accelerated when replacing the microscale boundary value problem by an efficient ML-based surrogate model [1] – as we will show for multiscale simulations of beam-based metamaterials, accelerated by a Kolmogorov-Arnold (KAN) network. Another example are constitutive artificial neural networks for architected materials, which learn the complex constitutive response as a function of the underlying structural architecture [2] for use as surrogate models in macroscale boundary value problems. Overall, we demonstrate how the integration of the principles of mechanics and thermodynamics with ML tools can result in (sufficiently) accurate and efficient AIaccelerated solutions for computational multiscale modeling. We close by highlighting the potential of such tools also for design optimization, e.g., of architected materials
A coupled Multiscale Model of the Human Cornea Accounting for the Collagenous Microstructure and the Extracellular Matrix
Get PDFThe human cornea is a complex, highly specialized structure necessary for the vision function of the Eye. The cornea, due to its shape and transparency, refracts and transmits the light to the retina. Cornea's mechanical properties, critical for maintaining corneal shape and function under intraocular pressure, arise from the composition of a hydrated proteoglycan-rich extracellular matrix (ECM) reinforced by an intricate network of collagen fibrils organized into lamellae. Despite extensive research, existing biomechanical models often fall short of capturing the coupled interplay between the ECM and collagen reinforcements, especially under physiological and pathological conditions. This work seeks to address this gap by proposing a novel computational model that integrates a continuum representation of the ECM with a discrete collagencrosslinking network. The continuum approach for the ECM is chosen to represent its viscoelastic behavior and interaction with fluid flow, critical for corneal hydration and load transmission. Conversely, the collagen network is modeled as a discrete, anisotropic reinforcement system, capturing the directional stiffness imparted by the collagen fibrils and their crosslinking. The model is developed to account for the influence of enzymatic degradation, age-related changes, and disease processes such as keratoconus, where alterations in the ECM-collagen coupling are known to drive structural instability. The innovation of this approach lies in its multiscale integration, bridging the molecular mechanics of collagen crosslinking with macroscopic corneal behavior. By explicitly linking the continuum matrix with a collagen-reinforced network, the model offers some possibility to deepen our understanding of corneal mechanics. The inclusion of experimentally derived parameters for collagen alignment, crosslink density, and ECM properties, will make the model predictive in the simulation of physiological responses to intraocular pressure and external mechanical perturbations
Plasticity and the airframe -- aircraft structure from birth to ultimate state
Get PDFFrom the beginning of metallic airframes in the 1930's until today, it has always been required to have a thorough understanding of the plastic behaviour of aircraft structure. Nowadays, advanced models of plasticity are used along the entire design and development process: they are needed to compute the ultimate state of the structure for certification, and also to simulate the manufacturing of the often complex, double-curved components. Mastery of this chain has helped to create the safest mode of transport humankind has known so far. This talk will recap some of the history that got us to where we are today, to help understand current practice, and it will sketch future needs and possibilities. As in the past, the collaboration between academic research and the aerospace industry will continue to be the key to safety, comfort and sustainability
Numerical Coupling of a FVM and FEM Codes Applied to a Low-Prandtl Turbulent Square Cavity
No full text
Matrix Algorithms Based on Jacobi and Romanovski-Jacobi Polynomials for Solving the FitzHugh-Nagumo Nonlinear Equation
Get PDFThe present paper develops and makes efficient, a new, state-of-theart numerical technique for solving the FitzHugh-Nagumo Nonlinear Equation (FH-NNE) with initial and boundary conditions, which represents perhaps the simplest mathematical model for discussing biological systems, including nerve signals and cardiac behavior. Which consists of operational matrices and spectral techniques based on Jacobi and Romanovski-Jacobi polynomials. It is ensured that the nonlinear system is modeled so accurately that it can be effectively solved to ensure the best accuracy combined with computational economy. Comparing the results with the respective numerical results, it is seen that the proposed techniques outdo the standard ones as respects accuracy and efficiency of computation.OPEN ACCESS Received: 11/01/2025 Accepted: 25/02/2025 Published: 07/04/202
A Sphalt Crack Recognition Algorithm Based on Fuzzy Automatic Threshold C-Means Clustering Algorithm
Get PDFCracks are the most significant type of pavement disease, and the precise segmentation of cracks serves as an important decision-making basis for national preventive road maintenance management. In response to the problem of crack segmentation accuracy of existing pavement models under complex backgrounds, a crack recognition algorithm for remote sensing images based on the Fuzzy Automatic Threshold C-Means Clustering Algorithm (FATCM) was designed by incorporating local spatial and gray-level information constraints. The FATCM method can strengthen the inherent effectiveness of the traditional fuzzy C-means (FCM) algorithm, achieve uniform segmentation through fuzzy membership calculation and iterative process, and effectively eliminate edge ambiguity. The core innovation of FATCM resides in the introduction of the fuzzy local similarity measure, which is predicated upon the pixel spatial attraction model. This novel measure is astutely applied to automatically strike a refined equilibrium. Specifically, it ensures a high degree of insensitivity to noise, a factor of paramount importance in safeguarding the integrity of image data. Simultaneously, it minimizes the manifestation of edge-blurring artifacts, thereby proficiently retaining the minute and crucial details of the image. Multiple types of images in the Crack500 dataset were used in the experiments to evaluate the performance of FATCM. The experimental results show that this method has good detection results and can effectively extract weakly contrasted cracks and small cracks.OPEN ACCESS Received: 24/07/2024 Accepted: 16/12/2024 Published: 20/04/202
