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

Harmonic-Modal Hybrid Reduced Order Model for the Efficient Integration of Non-Linear Soil Dynamics

Nonlinear behavior of soils during a seismic event has a predominant role in current site response analysis. Soil response analysis, and more concretely laboratory data, indicate that the stress-strain relationship of soils is nonlinear and exhibits hysteresis. An equivalent linearization method, in which non-linear characteristics of shear modulus and damping factor of soils are modeled as equivalent linear relations of the shear strain is usually applied, but this assumption, however, may lead to a conservative approach of the seismic design. In this paper, we propose an alternative analysis formulation, able to address forced response simulation of soils exhibiting their characteristic nonlinear behavior. The proposed approach combines ingredients of modal and harmonic analyses enabling efficient time-integration of nonlinear soil behaviors based on the offline construction of a dynamic response parametric solution by using Proper Generalized Decomposition (PGD)-based model order reduction technique

Learning stable reduced-order models for hybrid twins

The concept of “hybrid twin” (HT) has recently received a growing interest thanks to the availability of powerful machine learning techniques. This twin concept combines physics-based models within a model order reduction framework—to obtain real-time feedback rates—and data science. Thus, the main idea of the HT is to develop on-the-fly data-driven models to correct possible deviations between measurements and physics-based model predictions. This paper is focused on the computation of stable, fast, and accurate corrections in the HT framework. Furthermore, regarding the delicate and important problem of stability, a new approach is proposed, introducing several subvariants and guaranteeing a low computational cost as well as the achievement of a stable time-integration

Learning stable reduced-order models for hybrid twins

The concept of Hybrid Twin (HT) has recently received a growing interest thanks to the availability of powerful machine learning techniques. This twin concept combines physics-based models within a model-order reduction framework-to obtain real-time feedback rates-and data science. Thus, the main idea of the HT is to develop on-the-fly data-driven models to correct possible deviations between measurements and physics-based model predictions. This paper is focused on the computation of stable, fast and accurate corrections in the Hybrid Twin framework. Furthermore, regarding the delicate and important problem of stability, a new approach is proposed, introducing several sub-variants and guaranteeing a low computational cost as well as the achievement of a stable time-integration

Mathematical analysis and numerical resolution of a heat transfer problem arising in water recirculation

This work is devoted to the analysis and resolution of a well-posed mathematical model for several processes involved in the artificial circulation of water in a large waterbody. This novel formulation couples the convective heat transfer equation with the modified Navier-Stokes system following a Smagorinsky turbulence model, completed with a suitable set of mixed, nonhomogeneous boundary conditions of diffusive, convective and radiative type. We prove several theoretical results related to existence of solution, and propose a full algorithm for its computation, illustrated with some realistic numerical examples

Structural health monitoring by combining machine learning and dimensionality reduction techniques

Article number 20International audienceStructural Health Monitoring is of major interest in many areas of structural mechanics. This paper presents a new approach based on the combination of dimensionality reduction and data-mining techniques able to differentiate damaged and undamaged regions in a given structure. Indeed, existence, severity (size) and location of damage can be efficiently estimated from collected data at some locations from which the fields of interest are completed before the analysis based on machine learning and dimensionality reduction techniques proceed

Parametric evaluation of part distortion in additive manufacturing processes

Additive manufacturing is the more and more considered in industry, however efficient simulation tools able to perform accurate predictions are still quite limited. The main difficulties for an efficient simulation are related to the multiple scales, the multiple and complex physics involved, as well as the strong dependency on the process trajectory. This paper aims at proposing a simplified parametric modeling and its subsequent parametric solution for evaluating parametrically the manufactured part distortion. The involved parameter are the ones parametrizing the process trajectories, the thermal shrinkage intensity and anisotropy (the former depending on several material and process parameters and the last directly depending on the process trajectory) and the deposited layers. The resulting simulation tool allows evaluating in real-time the impact of the parameters just referred on the part distortion, and proceed to the required geometrical compensatio

A novel sparse reduced order formulation for modeling electromagnetic forces in electric motors

A novel model order reduction (MOR) technique is presented to achieve fast and real-time predictions as well as high-dimensional parametric solutions for the electromagnetic force which will help the design, analysis of performance and implementation of electric machines concerning industrial applications such as the noise, vibration, and harshness in electric motors. The approach allows to avoid the long-time simulations needed to analyze the electric machine at different operation points. In addition, it facilitates the computation and coupling of the motor model in other physical subsystems. Specifically, we propose a novel formulation of the sparse proper generalized decomposition procedure, combining it with a reduced basis approach, which is used to fit correctly the reduced order model with the numerical simulations as well as to obtain a further data compression. This technique can be applied to construct a regression model from high-dimensional data. These data can come, for example, from finite element simulations. As will be shown, an excellent agreement between the results of the proposed approach and the finite element method models are observed.We thank Professor Jose Roger-Folch (Univer-sitat Politècnica de València) for useful discussion, comments and feedback about the present work

Non-Intrusive In-Plane-Out-of-Plane Separated Representation in 3D Parametric Elastodynamics

Mesh-based solution of 3D models defined in plate or shell domains remains a challenging issue nowadays due to the fact that the needed meshes generally involve too many degrees of freedom. When the considered problem involves some parameters aiming at computing its parametric solution the difficulty is twofold. The authors proposed, in some of their former works, strategies for solving both, however they suffer from a deep intrusiveness. This paper proposes a totally novel approach that from any existing discretization is able to reduce the 3D parametric complexity to the one characteristic of a simple 2D calculation. Thus, the 3D complexity is reduced to 2D, the parameters included naturally into the solution, and the procedure applied on a discretization performed with a standard software, which taken together enable real-time engineering

Spurious-free interpolations for non-intrusive PGD-based parametric solutions: Application to composites forming processes

Non-intrusive approaches for the construction of computational vademecums face different challenges, especially when a parameter variation affects the physics of the problem considerably. In these situations, classical interpolation becomes inaccurate. Therefore, classical approaches for the construction of an offline computational vademecum, typically by using model reduction techniques, are no longer valid. Such problems are faced in different physical simulations, for example welding path problems, resin transfer molding, or sheet compression molding, among others. In such situations, the interpolation of precomputed solutions at prescribed parameter values (built using either intrusive or non intrusive techniques) generates spurious numerical artifacts. In this work, we propose an alternative interpolation and simulation strategy by using physically-based morphing of spaces. The morphing will transform the uncompatibe physical domains of the problem’s solution into a compatible one, where an interpolation free of artifacts can be performed. Later on, an inverse transformation can be used to push-back the solution. Different relevant examples are illustrated in this work to motivate the use of the proposed metho