We propose a Fast Fourier Transform based solver for estimating the thermoporomechanical behavior of porous materials, aiming at the periodic homogenization of microstructures with infinitely contrasted properties. Grounded on a thermoporomechanical framework, we first present the complete set of homogenized coupled properties. Subsequently, we isolate each physics, considering an elastoplastic formulation for the mechanical part, and fluid flow and heat conduction being governed by Darcy and Fourier laws before deriving the corresponding homogenized coupled operators. We employ, across the entire numerical framework, the Adaptive Eyre-Milton (AEM) algorithm as an enhanced iterative algorithm that guarantees convergence under infinitely contrasted material properties. Practical implementation aspects and illustrative applications in two-and three-dimensional settings are validated using analytical micromechanics solutions and a FEM-based solver. We emphasize the capability of the proposed framework to compute all the thermohydromechanical (THM) operators within a single tool while maintaining robustness even for materials with infinitely high contrasts in material properties for the three considered physics.
Traffic state estimation from sparse fixed sensors is challenging because physics-informed neural networks (PINNs) tend to over-smooth sharp transitions admitted by the Lighthill-Whitham-Richards (LWR) model. This study proposes Two-Stage Domain Decomposition Physics Informed Neural Networks (TSDD-PINN), an observation-aligned framework for LWR-based fline speed-field reconstruction. The framework supports spatial, temporal, and space-time refinement. Matched direction analysis shows that spatial refinement has the lowest mean error and less than half the training time of space-time refinement in the tested setting, while temporal finement is faster. A global parent PINN is first trained. In the controlled spatial implementation, its residual profile guides a deterministic partition for warm-started child networks. An optional operational safeguard retains Stage 1 when the prespecified screen does not activate. The primary I-24 MOTION evaluation spans six methods, five days, five sensor configurations, and ten seeds per configuration, yielding 1,500 runs. Controlled TSDD-PINN attains the lowest relative L2 error in 18 of 25 configurations and 14 of 15 sparse-sensing cases, while training 2.4 times faster than the extended PINN (XPINN) baseline under the evaluated implementations and training budgets. Non-neural comparisons show that the advantage over interpolation is concentrated under sparse sensing, whereas dense sensing often favors interpolation. A separate 250-run operational evaluation finds infrequent activation and motivates the Stage-1-preserving safeguard. The residual treated as an indicator of model difficulty rather than a validated shock detector. The evidence supports a sensing-density-dependent operating range rather than uniform improvement.
We present a variational reduced-order model for three-dimensional coplanar propagation of sharp cracks in heterogeneous perfectly brittle solids under mixed-mode I+II+III loading. The approach connects the variational fracture formulation of Francfort and Marigo (1998) and the perturbation theory of Rice (1985) by computing equilibrium crack-front configurations through minimization of the total energy defined as the sum of (i) the elastic potential energy, evaluated asymptotically from front deformations, and (ii) the dissipated energy, set by the fracture energy field. The potential energy and its derivatives are evaluated efficiently using the Fast Fourier Transform. The resulting nonconvex box-constrained minimization problem is solved with a matrix-free Newton conjugate gradient algorithm with a trust region and physics-based preconditioning, enforcing irreversibility while resolving energy barriers and long-range elastic interactions. We validate our implementation against newly derived analytical solutions. We then perform 116,000 large-scale simulations of tensile and shear crack propagation in disordered media to quantify the impact of finite-size effects, disorder intensity, and mode mixity. The simulations reproduce the transition from smooth to intermittent crack growth, and show that mode mixity has limited influence on the onset of intermittency but induces quasi-elliptic fronts in mixed II+III loading. They reveal a size-dependent crossover from disorder-induced weakening to toughening controlled by the emergence of depinning instabilities.
Structural adhesive bonding is being increasingly used for civil engineering applications (externally bonded reinforcement, bonded fasteners, etc ...). Yet, some issues remain regarding the assessment of the durability of such assemblies, particularly with regard to creep. For the case of adhesively bonded joints, creep can be affected by the type of polymer and its behaviour, the stress state and level, the temperature, but also by the interfaces and the thickness of the joint that has a direct influence on its mechanical properties. In this study, in order to take into account those different effects, the Arcan test was used to conduct experimental investigations with dedicated testing resources. The creep behaviour of a chosen adhesive joint was investigated and the influence of several parameters was studied: load level, type of load, and temperature. The adhesive thickness and surface preparation were kept constant. The three characteristic creep stages could be identified. The experimental investigations also enabled us to describe the creep behaviour of the adhesive joint in tension and shear during the first two stages using the Burger creep model.
