Effects of the dust size distribution on shock waves in dusty plasma

By using a Korteweg-deVries-Burgers(KdV-Burgers) equation and considering the dust size distribution, we have studied effects of the dust size distribution on the shock wave in dusty plasma. The dependence of characteristics of the shock wave on different dust size distributions has been given. It is found that the speed and amplitude of a shock wave considering the dust size distribution are larger than that of the dusty plasma with the averaged dust size. However, the width of a shock wave considering the dust size distribution is smaller than that of the dusty plasma with the averaged dust size.     

Modeling of the blocking temperature of a system of core/shell nanoparticles

A theoretical study was conducted on the size dependence of the blocking temperature T_b of a system of interacting core/shell nanoparticles. A method for estimating the blocking temperature of interacting core/shell nanoparticles is presented, which allows T_b to be calculated more precisely than using the “Neel relation”. It was shown that with an increase in the intensity of the magnetostatic interaction (concentration of nanoparticles), the blocking temperature increases, while the growth of the external magnetic field leads to the opposite effect. Moreover, the T_b of large nanoparticles changes more significantly. Comparing different approaches, we identify a precise method for determining the blocking temperature from ZFC and FC magnetization curves.     

Estimation of high-order moment-independent importance measures for Shapley value analysis

Moment-independent importance measures are increasingly used by practitioners to understand how output uncertainty may be shared between a set of stochastic inputs. Computing Borgonovo's sensitivity indices for a large group of inputs is still a challenging problem due to the curse of dimensionality and it is addressed in this article. An estimation scheme taking the most of recent developments in copula theory is developed. Furthermore, the concept of Shapley value is used to derive new sensitivity indices, which makes the interpretation of Borgonovo's indices much easier. The resulting importance measure offers a double advantage compared with other existing methods since it allows to quantify the impact exerted by one input variable on the whole output distribution after taking into account all possible dependencies and interactions with other variables. The validity of the proposed methodology is established on several analytical examples and the benefits in terms of computational efficiency are illustrated with real-life test cases such as the study of the water flow through a borehole. In addition, a detailed case study dealing with the atmospheric re-entry of a launcher first stage is completed.     

A network model for stemflow solute transport

While the role of stemflow in directing and concentrating water and nutrients at the tree base is rarely in dispute, its mathematical representation remains a subject of inquiry and research. A network model that seeks to estimate stemflow solute concentration and leaching is proposed. The model accommodates the physico-chemical properties of individual furrows embedded within the tree bark and their interconnections. The within-furrow equations for water and solute transport that include leaching are first developed and integrated along a rough-bark network topology to describe solute concentration and fluxes out of the network. The model is parameterized using published data on stemflow, field measurements of bark geometry, and laboratory experiments on bark leaching for potassium, magnesium, and calcium. The parameterization is intended to impose plausibility constraints and not to test model predictions at a particular site, a single event, or an individual experiment. The outflow concentration is then analyzed as a function of the network complexity that includes asymmetry in the lengths or subpaths connecting network nodes. For a symmetric network, an effective ’channel-flow’ analogy may be used to represent solute concentration at the outflow. However, as the asymmetry increases in subpath lengths, the efficiency of the bark network at moving solutes diminishes for the same rainfall input onto the stem. The network representation featured here is by no means offering a ’finality’ to the stemflow mathematical representation. It must be viewed as an embryonic step that opens up the possibility of using modern advances in network theories to link rainfall properties to stemflow water and solute input from a variety of tree species with differing bark microrelief configurations into the soil.     

A modified Artificial Bee Colony algorithm for structural damage identification under varying temperature based on a novel objective function

This paper presents a modified Artificial Bee Colony algorithm for structural damage identification. Meanwhile, the effect of temperature variation is considered and the change of temperature will lead to the alteration of Young's modulus of material. A novel objective function is proposed as the combinations of the partial mode shape curvature data, alterations of natural frequencies, and a sparse penalty term. Such an objective is found to be sensitive to structural damage while not sensitive to environmental effects. On the other hand, To render the standard Artificial Bee Colony algorithm more powerful and robustness, two local search strategies are introduced into the employed and onlooker bee phase of the Artificial Bee Colony algorithm, respectively. Two numerical examples and a laboratory verification are employed to verify the efficiency and advantage of the proposed algorithm. The final results show that the present algorithm could yield more satisfactory identification results compared with other state-of-the-art evolutionary algorithms, even high-level noise and temperature variation are considered; and the proposed novel objective function is more sensitive to structural damages, compared with the traditional mode-shape-based objective function.     

Two-phase flow simulation for distinguishing deformable particles with a LiMCA system

In the metallurgical industry, Liquid Metal Cleanliness Analyser (LiMCA) commercial equipment cannot distinguish between hard particles (e.g., oxides, borides) and deformable particles (e.g., bubbles, molten salts). Therefore, hard particle concentrations can sometimes be grossly overestimated, which reduces the measurement accuracy. However, the method could potentially discriminate between deformable particles and hard particles by evaluating the particle's ability to deform. In this work, the coupled multiphysics problem of a particle deforming within current-carrying aluminium metal passing through the electric sensing zone (ESZ) is simulated using the conservative level-set (CLS) method. An emphasis is placed on understanding the transient deformation history, and the effect of the capillary number, Reynolds number, and confinement ratio on deformation are studied. Furthermore, a computational basis is given to estimate the influence of particle deformation on electrical resistance pulses (ERP). It is found that ERP features of deformation particles, including the peak magnitude and the pulse width, are different from those of hard particles. Based on the results, the effect of a particle's deformation and the feasibility to discriminate it from non-deformable particles in the LiMCA system is evaluated.     

Robust optimal control of stochastic hyperelastic materials

Soft robots are highly nonlinear systems made of deformable materials such as elastomers, fluids and other soft matter, that often exhibit intrinsic uncertainty in their elastic responses under large strains due to microstructural inhomogeneity. These sources of uncertainty might cause a change in the dynamics of the system leading to a significant degree of complexity in its controllability. This issue poses theoretical and numerical challenges in the emerging field of optimal control of stochastic hyperelasticity. This paper states and solves the robust averaged control in stochastic hyperelasticity where the underlying state system corresponds to the minimization of a stochastic polyconvex strain energy function. Two bio-inspired optimal control problems under material uncertainty are addressed. The expected value of the L²-norm to a given target configuration is minimized to reduce the sensitivity of the spatial configuration to variations in the material parameters. The existence of optimal solutions for the robust averaged control problem is proved. Then the problem is solved numerically by using a gradient-based method. Two numerical experiments illustrate both the performance of the proposed method to ensure the robustness of the system and the significant differences that may occur when uncertainty is incorporated in this type of control problems.     

Fluid sensing using microcantilevers: From physics-based modeling to deep learning

In-situ measurements of the viscosity and density of small volumes of liquids are required in several industrial applications. MEMS sensors deploying vibrating microstructures constitute an attractive alternative given the significant impact of the surrounding liquid on their dynamic behavior. In this work, we combine physics-based modeling approaches and deep learning techniques to simultaneously estimate the density and viscosity of liquids from the resonance frequencies and quality factors of immersed microcantilevers. The physics-based model is first validated by comparing the simulated resonance frequencies and quality factors of immersed microcantilevers to those obtained from experiments conducted on a large variety of liquids. Then, we use the simulations results to train deep neutral networks to learn the mapping from the data space to the parameter space. The deep learning method shows high prediction accuracy provided that there is enough independent input data, shows no bias in the predicted values, and provides the results instantaneously. The optimal accuracy in the estimation of the liquid viscosity and density is achieved when the first resonance frequency and corresponding quality factor are used as inputs.     

Numerical study of bifurcation blood flows using three different non-Newtonian constitutive models

In this work, a variational multiscale finite element formulation is used to study bifurcation flows of non-Newtonian fluids, using a representative simplified Carotid Artery geometry. In particular, the flow pattern and wall shear stress (WSS) computed using power-law, Cross, and Carreau–Yasuda models, are assessed. First, the formulation is validated by contrasting simulations of a benchmark test for bifurcation flows reported in the literature. After that, a study of blood flow through the carotid artery is presented. Hemodynamics conditions aimed to describe the flow behavior from diastole to systole of the cardiac cycle for healthy arteries and two specific conditions (60% carotid stenosis due to atherosclerosis and 20% increased bifurcation angle due to aging), are specifically analyzed. For each condition, the hemodynamics present different velocity fields that lead to distinctive distribution of WSS enable us to classified three regions, depending on their magnitude: low-WSS, medium-WSS and high-WSS. Results show that power-law flows predict lower wall shear stresses, especially in sections where geometry concentrates stresses, compared to those predicted using Cross and Carreau–Yasuda models. Overall, low-WSS are usually present in zones where stenosis develops even in healthy arteries, however, both geometries lead to a decrease of WSS magnitude in low-WSS regions, increasing the risk factor associated with plaque building.