Computer Methods in Applied Mechanics and Engineering

Addition to the paper

The files are the programs of the novel interval analysis approach and the new nonlinear interval number programming method.

This excel file provides the data obtained from Abaqus simulations that is reported in the following paper entitled "A stabilized finite element method for enforcing stiff anisotropic cohesive laws using interface elements," which is submitted for publication to Computer Methods in Applied Mechanics and Engineering.

Model implementation in MATLAB language and scripts to reproduce test cases described in the paper MODELING BLOOD FLOW IN VISCOELASTIC VESSELS: THE 1D AUGMENTED FLUID-STRUCTURE INTERACTION SYSTEM.

Data generated from DEM assembles used in the meta-modeling algorithm.

MATLAB figure files corresponding to the figures in the article

Addition to the paper

Data for: A novel dynamic isogeometric reanalysis method and its application in closed-loop optimization problems

Addition to the paper

This data repository contains generated data files from the experiments in the paper:A. Wulff et al.: Comput. Methods Appl. Mech. Eng. 432 (2024) 117380,doi: 10.1016/j.cma.2024.117380 Abstract:As with many tasks in engineering, structural design frequently involves navigating complex and computationally expensive problems. A prime example is the weight optimization of laminated composite materials, which to this day remains a formidable task, due to an exponentially large configuration space and non-linear constraints. The rapidly developing field of quantum computation may offer novel approaches for addressing these intricate problems. However, before applying any quantum algorithm to a given problem, it must be translated into a form that is compatible with the underlying operations on a quantum computer. Our work specifically targets stacking sequence retrieval with lamination parameters, which is typically the second phase in a common bi-level optimization procedure for minimizing the weight of composite structures. To adapt stacking sequence retrieval for quantum computational methods, we map the possible stacking sequences onto a quantum state space. We further derive a linear operator, the Hamiltonian, within this state space that encapsulates the loss function inherent to the stacking sequence retrieval problem. Additionally, we demonstrate the incorporation of manufacturing constraints on stacking sequences as penalty terms in the Hamiltonian. This quantum representation is suitable for a variety of classical and quantum algorithms for finding the ground state of a quantum Hamiltonian. For a practical demonstration, we performed numerical state-vector simulations of two variational quantum algorithms and additionally chose a classical tensor network algorithm, the DMRG algorithm, to numerically validate our approach. For the DMRG algorithm, we derived a matrix product operator representation of the loss function Hamiltonian and the penalty terms. Although this work primarily concentrates on quantum computation, the application of tensor network algorithms presents a novel quantum-inspired approach for stacking sequence retrieval. For further information on the data in this repository, view the 'README.md' file.