Cross-laminated timber (CLT) is gaining popularity as a sustainable building material due to its favorable structural performance and low environmental impact. This study introduces a multiscale domain decomposition method, known as the LaTIn strategy, to predict the out-of-plane behavior and failure of CLT panels. The proposed approach incorporates cohesive zone models to capture inter- and intralaminar damage using a cohesive parameter identification based on strength values reported in the literature. By aligning domain partitioning with principal stress directions and leveraging parallel computations, the method reduces degrees of freedom and the number of subdomains by up to 55% and 70%, respectively. Additional algorithmic enhancements allow for a 70% reduction of iterations. Validation with experimental data confirms the model’s ability to predict rolling shear failure with high accuracy. These results demonstrate the potential of the strategy for efficient and reliable simulation of timber structures, in line with the needs of multiscale nonlinear analysis in structural engineering.

Abstract This study investigates Wire Arc Spraying Atomization (WASA) of Ag925 powders through experiments and computational fluid dynamics simulations. Two frameworks were evaluated: a conventional Discrete Phase Model (DPM) and a hybrid Volume-of-Fluid–Discrete Phase Model (VOF–DPM) with adaptive mesh refinement. Experimental powders exhibited predominantly spherical morphology with a particle size distribution centered around 45–50 μm. Statistical goodness-of-fit tests (Chi-square, Kolmogorov–Smirnov, Anderson–Darling) confirmed that both simulations deviate significantly from experiment, though VOF–DPM consistently achieves closer agreement. In addition to improved predictions of surface-area–weighted mean diameter (D 32 ), VOF–DPM reproduced morphological features such as irregular breakup and circularity values more consistent with experiment, unlike the idealized sphericity assumed in DPM. These results highlight the importance of resolving primary and secondary breakup mechanisms to capture both PSD and morphology. Limitations include constant material properties, reduced computational domains, and simplified particle assumptions, which suggest clear directions for refinement in future work.

Abstract We present a fast first order explicit time integration scheme for solving parabolic problems in mechanics via standard numerical methods in space using irregular grids, such as unstructured finite element meshes, or grids containing elements or cells of very different sizes. The new scheme extends one of the explicit FIC-Time (EFT) integration methods derived by the authors in [23] that allow considerable larger time steps than the forward-Euler (FE) scheme. The new EFT scheme overcomes the limitations in the time step size of explicit time integration schemes for irregular grids containing large and small elements. A variable time step is used for eliminating the oscillations near Dirichlet boundaries when large time steps are used. The advantages of the new EFT scheme versus the FE scheme are shown in one-, two- and three-dimensional transient heat conduction problems using irregular finite element grids.

Abstract This paper explores possible implementations of a quantum annealing-based algorithm, in the Quadratic unconstrained binary optimization method (QUBO) form, to solve the thermo-fluid dynamics problem associated with the design of critical components of aircraft environmental control systems, namely compact heat exchangers and ejectors (also known as jet pumps), used on rotary- or fixed- winged aircrafts environmental control system to mix two fluids and exchange thermal energy. The purpose of this paper is to introduce the implementation of the QUBO model based on compact heat exchangers lumped model and the SAE 1168–6 ejector design procedure, with the formalization of the mathematical problem and the comparison of results obtained with experimental tests, classical resolution approaches and the quantum optimized code, showing how the QUBO approach is an effective and efficient tools for design engineers tasked to design and validate equipment parameters.We start with a simple two-phase flow within a single-pass mini-channel heat exchanger, describing the mathematical formulation of the problem followed by a simplified implementation of the proposed quantum approaches, highlighting the main procedures of the calculation as well as the practical implications, advantages and disadvantages of the proposed method, e.g. a limited number of CFD simulations are required to evaluate air side heat transfer coefficient and pressure drop for particular designs within the defined design space, and the specific hardware required to implement the quantum process.

A variationally consistent numerical approach based on the Virtual Element Method (VEM) is presented for the analysis of 2D elastoplasticity problems. The mixed Hu-Washizu functional of elasticity is extended to incorporate the energy contributions specific to the finite-step elastoplastic problem. It is demonstrated how the governing equations of the discretized elastoplastic problem - including the loading-unloading conditions - emerge naturally as the stationarity conditions of the VEM-discretized functional. Spurious hourglass modes are prevented by formulating a self-stabilized version of Virtual Elements (VEs) that exploits the possibility offered by the mixed approach to define strain and displacement approximations of the same order. The insensitivity of VEs to element distortion and the possibility to use polygonal elements with any shape and number of edges is tested with the analysis of several benchmarks from the literature. It is shown how accurate solutions can be obtained also in the case of non-convex quadrilateral or pentagonal elements. Additionally, the role of internal moment degrees of freedom in preventing elastoplastic locking at the plastic failure limit is elucidated.

Investigating soil swelling potential is indeed a critical research area in geotechnical engineering, given its significant influence on the stability and longevity of civil structures. This study aims to predict and assess the one-dimensional vertical free swelling potential of soils using ensemble machine learning models. Within the study context, a large dataset encompassing a wide array of soil parameters from 210 soil samples, including moisture content, unit weight, plasticity, and clay content, will be used. These parameters are critical in understanding the swelling behavior of soils under varying environmental and load conditions. The novel approach of this research lies in the application of ensemble machine learning techniques, which offer a robust framework to analyze complex, nonlinear relationships within soil properties. Another key aspect of this research is the parametric assessment, where the influence of individual soil properties on swelling potential is investigated using feature importance and partial dependence analyses. These analyses provide valuable insights into the relative importance of different soil parameters on soil behavior. The outcomes of this study contribute to soil mechanics and machine learning applications in geotechnical engineering and offer practical implications for engineers and practitioners. Besides, the predictive models developed in this study aid in more informed decision-making in the design and construction of civil structures, particularly in swelling-prone areas.

This study introduces a surrogate modeling framework merging proper orthogonal decomposition, long short-term memory networks, and multi-task learning, to accurately predict elastoplastic deformations in real-time. Superior to single-task neural networks, this approach achieves a mean absolute error below 0.40% across various state variables, with the multi-task model showing enhanced generalization by mitigating overfitting through shared layers. Moreover, in our use cases, a pre-trained multi-task model can effectively train additional variables with as few as 20 samples, demonstrating its deep understanding of complex scenarios. This is notably efficient compared to single-task models, which typically require around 100 samples. Significantly faster than traditional finite element analysis, our model accelerates computations by approximately a million times, making it a substantial advancement for real-time predictive modeling in engineering applications. While it necessitates further testing on more intricate models, this framework shows substantial promise in elevating both efficiency and accuracy in engineering applications, particularly for real-time scenarios.

We illustrate an application of graph neural networks (GNNs) to predict the pressure, temperature and velocity fields induced by a sudden explosion. The aim of the work is to enable accurate simulation of explosion events in large and geometrically complex domains. Such simulations are currently out of the reach of existing CFD solvers, which represents an opportunity to apply machine learning. The training dataset is obtained from the results of URANS analyses in OpenFOAM. We simulate the transient flow following impulsive events in air in atmospheric conditions. The time history of the fields of pressure, temperature and velocity obtained from a set of such simulations is then recorded to serve as a training database. In the training simulations we model a cubic volume of air enclosed within rigid walls, which also encompass rigid obstacles of random shape, position and orientation. A subset of the cubic volume is initialized to have a higher pressure than the rest of the domain. The ensuing shock initiates the propagation of pressure waves and their reflection and diffraction at the obstacles and walls. A recently proposed GNN framework is extended and adapted to this problem. During the training, the model learns the evolution of thermodynamic quantities in time and space, as well as the effect of the boundary conditions. After training, the model can quickly compute such evolution for unseen geometries and arbitrary initial and boundary conditions, exhibiting good generalization capabilities for domains up to 125 times larger than those used in the training simulations.

This paper explores the ability of physics-informed neural networks (PINNs) to solve forward and inverse problems of contact mechanics for small deformation elasticity. We deploy PINNs in a mixed-variable formulation enhanced by output transformation to enforce Dirichlet and Neumann boundary conditions as hard constraints. Inequality constraints of contact problems, namely Karush–Kuhn–Tucker (KKT) type conditions, are enforced as soft constraints by incorporating them into the loss function during network training. To formulate the loss function contribution of KKT constraints, existing approaches applied to elastoplasticity problems are investigated and we explore a nonlinear complementarity problem (NCP) function, namely Fischer–Burmeister, which possesses advantageous characteristics in terms of optimization. Based on the Hertzian contact problem, we show that PINNs can serve as pure partial differential equation (PDE) solver, as data-enhanced forward model, as inverse solver for parameter identification, and as fast-to-evaluate surrogate model. Furthermore, we demonstrate the importance of choosing proper hyperparameters, e.g. loss weights, and a combination of Adam and L-BFGS-B optimizers aiming for better results in terms of accuracy and training time.