Radial Basis Functions Vector Fields Interpolation for Complex Fluid Structure Interaction Problems

Groth, Corrado; Porziani, Stefano; Biancolini, Marco Evangelos

doi:10.3390/fluids6090314

Cited by 9 publications

(4 citation statements)

References 56 publications

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“…This technique has exhibited reliability across various engineering domains, from the stress state recovery of composite plates [16,17] to fracture mechanics [18], FEM upscaling [19] and shape optimisation [20]. RBF-based mesh morphing can be also used, in view of its fast mesh update characteristics, for steady and unsteady FSI studies [21,22,23].…”

Section: Introductionmentioning

confidence: 99%

Stress mitigation of a thermal engine head block using the bioinspired BGM-FEM method

Groth,

Marra,

Porziani

et al. 2024

IOP Conf. Ser.: Mater. Sci. Eng.

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Structural optimization plays a pivotal role in the design and development of engine heads, as it directly affects the performance, efficiency, and durability of internal combustion engines. In this paper, we propose a novel approach for the thermo-structural optimization of the internal surfaces of the engine heads, using the Biological Growth Method (BGM). The BGM is a bio-inspired technique that mimics the growth patterns observed in some biological organisms, able to achieve superior structural efficiency thus generating optimal designs. The effectiveness of the BGM methodology, coupled with mesh morphing techniques based on Radial Basis Functions (RBF), is in this work demonstrated for several districts, effectively reducing material usage and enhancing structural performance.

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Section: Introductionmentioning

confidence: 99%

Stress mitigation of a thermal engine head block using the bioinspired BGM-FEM method

Groth,

Marra,

Porziani

et al. 2024

IOP Conf. Ser.: Mater. Sci. Eng.

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“…This mesh-less technique allows to generate modified configuration using shape modifications without the need to generate the underlying modified geometry. RBF mesh morphing has been proved to be reliable and time-saving in several advanced workflows, such as Fluid-Structure Interaction (FSI) [3,4,5,6,7,8], or crack front evolution [9,10], but also in stress recovery in composite plates [11,12] in fracture mechanics applications [13], FEM results upscaling [14] and shape optimization [15].…”

Section: Introductionmentioning

confidence: 99%

Advancing Structural Optimization of an Electric Motor Rotor through Mesh Morphing Techniques

Porziani,

Groth,

Zhang

et al. 2024

IOP Conf. Ser.: Mater. Sci. Eng.

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The electric motor has been one of the most important inventions of the last two centuries and has become increasingly important in the last 10 years thanks to its efficiency and its ability to reduce pollution. Given this increasing importance of electrical motors, an interest in design and optimization approaches for motor components is arising. Given the different physics involved in the transformation from electrical to mechanical energy, a valuable and reliable approach has to be applied in the optimization process. In past years, Mesh Morphing based on Radial Basis Functions (RBF) has largely proved its validity in generating shape modification for Finite Element Method (FEM) models. In this work, authors will demonstrate the advantages in applying two opposite approaches for shape optimization using RBF Mesh Morphing applied to electrical motors rotors. These components need to be accurately designed in order to guarantee an adequate life duration by reducing stresses in the rotating components. The two approaches that will be described are the parameter based one, in which a set of design parameters will be varied to generate shape modification used to feed a meta-model that will be used to identify an optimal configuration, and a parameter-less approach, in which the shape modification will be driven by FEM analysis results, with the aim to reduce stress hot-spots.

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“…Such functions can be linear or nonlinear with respect to the input variables. The radial basis functions can handle the behaviour of the system based on the selected function [1,2]. Based on a prior knowledge the posterior of deflection can be estimated by using Bayesian linear parameter model taking into account an uncertainty of estimation.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Regression Based Approach for Beam Deflection Estimation

Batbooti,

Mohammed,

Jabbar

et al. 2023

Adv. Sci. Technol. Res. J.

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Deflection of a beam is the movement of the beam from its initial position to another position depending on the applied load. Beam deflection estimation gives an indication about the possible deformation of the beam. A parametric Bayesian linear based model is introduced to mimic the experimentally collected data to estimate the stochastic deflection of a simply supported beam. A Gaussian noise is assumed to understand the stochastic behavior of the beam deflection as well as a Gaussian prior. The model mapping function used in this work is known as radial basis function, which can be linear or nonlinear. Three basis functions are compared, namely are linear, Gaussian and modified Gaussian function proposed in this work. The modified Gaussian function is a simple function introduced in this work. The performance of the functions is analyzed for three central concentrated loads. The best model can describe the observed data is found to be the modified Gaussian model with regularization factor of 0.9 for three loading cases. The prediction based linear basis function is better than the use of the Gaussian basis function prediction according to error of estimation. The maximum RMS error obtained for modified Gaussian radial basis function corresponding to central load of 4 kg is smaller than that of a theoretical based model for the same loading conditions.

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Radial Basis Functions Vector Fields Interpolation for Complex Fluid Structure Interaction Problems

Cited by 9 publications

References 56 publications

Stress mitigation of a thermal engine head block using the bioinspired BGM-FEM method

Stress mitigation of a thermal engine head block using the bioinspired BGM-FEM method

Advancing Structural Optimization of an Electric Motor Rotor through Mesh Morphing Techniques

Bayesian Regression Based Approach for Beam Deflection Estimation

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