3D modeling of the vane test on a power-law cement paste by means of the proper generalized decomposition

The effective modeling of the flow of fresh concrete materials in settings such as that of the vane test is a challenging process that is the object of ongoing research. Previous works modeled concrete and cement pastes as solids subjected to yielding or as Bingham or power-law fluids, both in two or three dimensions [1, 2]. Of the existing models, those implementing power-law fluids in three dimensions carry the best predictive ability considering the typically heterogeneous composition of concrete suspensions and the relatively complex three-dimensional features of their flows. In this work, we model the vane test in a power-law cement paste using the Proper Generalized Decomposition (PGD). In this framework, the three-dimensional problem is solved as a sequence of 2D × 1D problems, thus alleviating the curse of dimensionality. This choice is supported by experience from previous works using the PGD to simulate Non-Newtonian behavior using iterative resolutions [3, 4]. It is also particularly useful in addressing the inverse problem corresponding to the identification of the material properties of cement pastes from experimental data, as this requires many direct resolutions of the forward problem. The use of the PGD is also appealing because the model parameters can be introduced as extra coordinates of the problem [5]

Advanced modeling of the out-of-autoclave thermoplastics prepreg consolidation

Nowadays, composite materials are replacing metallic ones thanks to their excellent mechanical performances and reduced weight. However, many difficulties are encountered during composite forming processes. In fact, autoclave curing process is too expensive and limits the part size to the autoclave dimensions. Out-Of-Autoclave processes reduce substantially the cost of forming processes. However, the absence of autoclave pressure in out-of-autoclave manufacturing processes leads nowadays to high porosity and poor consolidation at the interface between the tows [1]. Moreover, the effect of the process parameters on the consolidation is still unknown and thus controlling the final parts quality is not obvious. Despite the high potential offered by the Out-of- Autoclave processes, only few researches has been made in the last few years, in order to quantify the consolidation of the tows while using such processes [2]. In fact, only few models addressing void dynamics in thermoplastic composites has been carried out [3, 4]. In this work, we are using a novel coupled approach involving modeling and simulation in order to quantify the consolidation in Out-of-Autoclave processes. Advanced model reduction techniques (POD, PGD ...) are employed in order to predict thermal fields during manufacturing processes and coupled to the subsequent squeeze flow

Editorial: Advanced materials modeling combining model order reduction and data science

Editorial on the Research Topic: Advanced materials modeling combining model order reduction and data science. Materials modeling has always been a challenging issue..

A parametric transfer function for real-time simulation of coupled complex problems

Industrial production lines often involve multistage manufacturing processes with coupled boundary conditions. The output of a process is the input of another processing stage. The end product of such production line is complicated to optimize since its simulation includes countless number of parameters and degrees of freedom. Therefore, incorporating all the end product parameters as extra coordinates of the problem is still an intractable approach, despite the recent advances in computation power and model order reduction techniques. In this work, we explore an alternative approach using a physically based mechanical transfer function method, which expresses all the physics of the problem in a single function. All part external effects, including boundary conditions for example, become an input of such function. The output result of the proposed function is a real-time simulation of the consider product, for any possible input set of parameters

Modeling the human knee joint using the Proper Generalized Decomposition

Nowadays, human joints specifically movable are active research topics. The lack of effective replacements and the inefficient natural healing of these joints hinders any athlete from pursuing his career if injured in his joints. Therefore, researchers are testing innovative soft materials and biphasic materi- als as replacements of human joints. However, the lack of effective mechanical modeling is slowing the development of new replacements. In this work, we tackle the mechanical modeling of the synovial joint in a human knee. The tibiofemoral joint is modelled during impact. This joint is basically made of a cartilage, a meniscus (both a biphasic material) and the synovial fluid. The modeling is performed using Brinkman equation. However, the rich physics in- volved in the thickness direction requires a large number of degrees of freedom in the mesh to represent the physical phenomenon taking place in a knee joint. Thus, the use of model order reduction techniques appears to be an appealing approach in this situation. In fact, the proper generalized decomposition re- duced the number of degrees of freedom by using domain decomposition. The result of this work shows the pressure and fluid flow in the synovial joint under impact. A post treatment of the solution estimates the force held by each of the fluid and solid components of the cartilage joint. This model could be used to the human knee to estimate its components’ velocities and pressure fields while performing an activity

Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems

We develop inductive biases for the machine learning of complex physical systems based on the port-Hamiltonian formalism. To satisfy by construction the principles of thermodynamics in the learned physics (conservation of energy, non-negative entropy production), we modify accordingly the port-Hamiltonian formalism so as to achieve a port-metriplectic one. We show that the constructed networks are able to learn the physics of complex systems by parts, thus alleviating the burden associated to the experimental characterization and posterior learning process of this kind of systems. Predictions can be done, however, at the scale of the complete system. Examples are shown on the performance of the proposed technique.Comment: 9 pages, 5 figure