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Highlights • Cost-effective strategies for developing surrogate models for drag on particles in shocked flows are explored. • A machine learning technique is used to develop the surrogate models from simulations based training data. • The training data are obtained from 3D simulations of shock-particle interaction. • The current study shows that the cost of developing simulation-based surrogate models can be reduced by using the multi-fidelity techniques. Abstract Macroscale simulations of shocked particulate flows rely on closure laws to model momentum transfer between the fluid and dispersed particles phase. Developing closure models from experimental data is expensive. Robust and accurate closures laws can be obtained through surrogate modeling using high-resolution mesoscale simulations. However, development of surrogate models for drag from 3D high-fidelity simulations of shock interaction with clusters of particles can be computationally prohibitive. This paper explores various strategies to efficiently construct surrogate models for drag on particles in the shocked flow. The cost of generating training data is reduced by selecting optimal grid resolutions, particle arrangements in clusters, and size of particle clusters, i.e., by selecting suitable representative volumes (RVEs). Different surrogate modeling strategies such as multi-fidelity and parameter-by-parameter construction approaches are examined. The surrogate models obtained from the different methods are compared to determine the most cost-effective machine learning based surrogate modeling method in the context of shock-particle interactions. [ABSTRACT FROM AUTHOR]

Abstract: The current investigation focuses on the determination of the strength of adhesive-bonded single lap joints under impact with the use of a split Hopkinson pressure bar (Kolsky bar). For this, experiments were conducted at different loading rates, for identical metallic adherends bonded by a two-part epoxy adhesive. Four different types of specimens were adopted, all with a given adhesive thickness. The length of overlap and the width of the adherends were varied resulting in four different areas of overlap. It was found that the average strength, as calculated from the readings obtained from a Kolsky bar, increases with decrease of overlap area. An elastodynamic model for the shear strain of the adhesive-bonded single lap joint was developed to investigate this drastic effect of overlap area on the average strength of the joint. The mathematical model was found to be dependent on both the material properties of the adherend and adhesive, as well as the structural properties of the joint, viz. the width and the thickness of the adhesive layer. A combined experimental-numerical technique was used to predict the strain distribution over the length of the bond in the adhesive. It was found that the edges of the adhesive were subjected to maximum strain, while a large part of the adhesive was found to exhibit zero shear strain. The effect of the lap length and the width was studied individually. The cumulative effect of averaging the strain over the entire overlap area, was decreased shear strain for an increased overlap area. The Kolsky bar was identified to give conservative values of the shear strength of an adhesive bonded lap joint under high rates of loading. [Copyright &y& Elsevier]

Abstract: In the current investigation, the effect of specimen size in a Kolsky Bar experiment has been analyzed. By a series of experiments, it has been shown that while determining the dynamic stress-strain relationship of a material using a non-cylindrical specimen, a judicious choice of specimen dimensions helps overcome size effects and estimates the flow stress of the material with reasonable accuracy. Similarly a dependence of the strength of the joint on the overlap area has been observed while finding the dynamic strength of an adhesive-bonded single-lap joint. [Copyright &y& Elsevier]