A mesoscale approach to simulate residual deformations in complex laser welding processes

Laser welding can be characterized by very small radii of beam, in the order of tenths of a millimeter, and very short high power inputs (more kW in few ms), and thus, it can be certainly classified as a microscale process with a high level of physical complexity. This is clearly incompatible, due to the high computational costs, with the analysis of macroscale processes related to large geometries and non-uniform welding patterns. In order to overcome this issue, a simplified finite element method (FEM)–based thermo-elastoplastic model is presented to simulate heat transfer and residual deformations due to thermal expansion and material plasticity. The idea is to substitute the microscale analysis with a mesoscale approach that renounces to describe in detail all the physical phenomena occurring in the heated zone and focuses attention on the correct prediction of the keyhole depth and weld pool size, that are the most important para meters to describe the mechanical characteristics of the welded joint. The concept of passive element, based on the numerical adjustment of the material properties in order to take into account the orthotropic behavior during the key hole formation, is introduced. In particular, the new approach has been tested on the pulsed laser welding process of two overlapping DC04 steel plates with thickness of 0.5 mm (so-called sandwich) and validated through experimental tests involving different input parameters, such as power, pulse duration and frequency, speed, and geometrical pattern

Multifidelity Recursive Cokriging for Dynamic Systems' Response Modification

In order to perform the accurate tuning of a machine and improve its performance to the requested tasks, the knowledge of the reciprocal influence among the system\u2019s parameters is of paramount importance to achieve the sought result with minimum effort and time. Numerical simulations are an invaluable tool to carry out the system optimization, but modeling limitations restrict the capabilities of this approach. On the other side, real tests and measurements are lengthy, expensive, and not always feasible. This is the reason why a mixed approach is presented in this work. The combination, through recursive cokriging, of low-fidelity, yet extensive, numerical model results, together with a limited number of highly accurate experimental measurements, allows to understand the dynamics of the machine in an extended and accurate way. The results of a controllable experiment are presented and the advantages and drawbacks of the proposed approach are also discussed

Uncertainty Quantification of Turbulence Model Applied to a Cavitating Hydrofoil

This paper presents the Global Sensitivity Analysis of the coefficients of the standard k-ε turbulence model used in RANS (Reynolds Averaged Navier-Stokes) simulations aimed to predict the flow around a bi-dimensional hydrofoil operating at non-cavitating and cavitating flow regimes. The sensitivity analysis is treated by the Sobol Decomposition, where the Sobol Indices are computed through the Polynomial Chaos Expansion of the 2-nd order with a Point-Collocation Non-Intrusive approach. From the current results, it seems that the considered cavitating flow regime is less sensitive to the variability of the input parameters, at least for the prediction of lift and drag

Distributed Lagrange Multiplier Functions for Fictitious Domain Method with Spectral/hp Element Discretization

A fictitious domain approach for the solution of second-order linear differential problems is proposed; spectral/hp elements have been used for the discretization of the domain. The peculiarity of our approach is that the Lagrange multipliers are particular distributed functions, instead of classical \u3b4 Dirac (impulsive)multipliers. In this paper we present the formulation and the application of this approach to 1D and 2D Poisson problems and 2D Stokes flow (biharmonic equation)

Multi-fidelity Gaussian process regression for prediction of random fields

We propose a new multi-fidelity Gaussian process regression (GPR) approach for prediction of random fields based on observations of surrogate models or hierarchies of surrogate models. Our method builds upon recent work on recursive Bayesian techniques, in particular recursive co-kriging, and extends it to vector-valued fields and various types of covariances, including separable and non-separable ones. The framework we propose is general and can be used to perform uncertainty propagation and quantification in model-based simulations, multi-fidelity data fusion, and surrogate-based optimization. We demonstrate the effectiveness of the proposed recursive GPR techniques through various examples. Specifically, we study the stochastic Burgers equation and the stochastic Oberbeck\u2013Boussinesq equations describing natural convection within a square enclosure. In both cases we find that the standard deviation of the Gaussian predictors as well as the absolute errors relative to benchmark stochastic solutions are very small, suggesting that the proposed multi-fidelity GPR approaches can yield highly accurate results

Preliminary experimental data analysis for Digital Twin development of a large bore Dual-Fuel engine

In recent years, digital models, and in particular Digital Twins (DTs), have seen a growing interest due to their ability to provide support in the development of more efficient systems and processes. This study presents the preliminary steps taken to develop a DT model for a marine Large Bore Dual-Fuel engine manufactured by Wärtsilä. The correlation between dependend and independent data set variables is presented in order to map the engine behaviour and validate the DT model before becoming operational. The analyses are conducted using the engine in gas mode, operating at 85% of Load (at the nominal speed of 600rpm). This operating point represents the typical target design for constant speed applications. The engine efficiency, emissions and combustion chamber parameters are investigated by varying the air-fuel mixture pressure, timing and duration parameters. Sensitivity analysis presents a tight relation between Nitrogen Oxides and Hydrocarbons (HC) emissions by varying the Scavenging Air Pressure. The HC emission function around the nominal value of the Pilot Fuel Injection Duration reverse its trend, while In Cylinder-Pressure and Combustion Duration functions presents opposite gradients. By advancing the Pilot Fuel Injection timing is shown an increase in Engine Efficiency respect to others input parameters

Analysis of geometric uncertainties in CFD problems solved by RBF-FD meshless method

In order to analyze incompressible and laminar fluid flows in presence of geometric uncertainties on the boundaries, the Non-Intrusive Polynomial Chaos method is employed, which allows the use of a deterministic fluid dynamic solver. The quantification of the fluid flow uncertainties is based on a set of deterministic response evaluations, which are obtained through a Radial Basis Function-generated Finite Differences meshless method. The use of such deterministic solver represents the key point of the analysis, thanks to the computational efficiency and similar accuracy over the traditional mesh-based numerical methods. The validation of the proposed approach is carried out through the solution of the flow past a 2D spinning cylinder near a moving wall and the flow over a backward-facing step, in presence of stochastic geometries. The applicability to practical problems is demonstrated through the investigation of geometric uncertainty effects on the forced convection of Al2O3-water nanofluid laminar flow in a grooved microchannel

Fictitious Domain approach with hp-finite element approximation for incompressible fluid flow

We consider the application of Fictitious Domain approach combined with least squares spectral elements for the numerical solution of fluid dynamic incompressible equations. Fictitious Domain methods allow problems formulated on a complicated shaped domain \u3a9 to be solved on a simpler domain \u3a0 containing \u3a9. Least Squares Spectral Element Method has been used to develop the discrete model, as this scheme combines the generality of finite element methods with the accuracy of spectral methods. Moreover the least squares methods have theoretical and computational advantages in the algorithmic design and implementation. This paper presents the formulation and validation of the Fictitious Domain Least Squares Spectral Element approach for the steady incompressible Navier\u2013Stokes equations. The convergence of the approximated solution is verified solving two-dimensional benchmark problems, demonstrating the predictive capability of the proposed formulation

Fictitious Domain Approach to Explore Multi Geometric Uncertainties by Different Polynomial Chaos Methodologies

3no availablenonenonePEDIRODA V; POLONI C.; PARUSSINI LPediroda, Valentino; Poloni, Carlo; Parussini, Luci

Discrete element simulation of the charge in the hopper of a blast furnace, calibrating the parameters through an optimization algorithm

The purpose of this study is to simulate the distribution of a coarse granular material discharged in a hopper via a conveyor belt. This simulation is intended to be a model calibration for an optimization that will be later performed to obtain a proper material distribution device. From the hopper, the material is discharged in a blast furnace. Hence, achieving an adequate distribution in the hopper is crucial, since that distribution is directly linked to how the material spreads in the blast furnace, and this heavily influences the efficiency of the whole steel-making process. The apparatus is modeled by online three dimensional Computer-Aided Design software Onshape. Rocky DEM, a Computer-Aided Engineering software based on Discrete Element Method, is used to simulate the charge. The parameters of the numerical model are calibrated through an optimization algorithm. This phase is realized thanks to the optimization platform modeFRONTIER, using an algorithm that exploits meta-models to reduce the computational time of the optimization. By comparing the simulated results with the visual data obtained from blast furnace plant, the goal is to validate the model and to better understand the behavior of the whole charging process