Abstract: We outline a methodology for the simulation of two-way coupled particle-laden flows. The drag force that couples fluid and particle momentum depends on the undisturbed fluid velocity at the particle location, and this latter quantity requires modelling. We demonstrate that the undisturbed fluid velocity, in the low particle Reynolds number limit, can be related exactly to the discrete Green's function of the discrete Stokes equations. In addition to hydrodynamics, the method can be extended to other physics present in particle-laden flows such as heat transfer and electromagnetism. The discrete Green's functions for the Navier–Stokes equations are obtained at low particle Reynolds number in a two-plane channel geometry. We perform verification at different Reynolds numbers for a particle settling under gravity parallel to a plane wall, for different wall-normal separations. Compared with other point-particle schemes, the Stokesian discrete Green's function approach is the most robust at low particle Reynolds number, accurate at all wall-normal separations. To account for degradation in accuracy away from the wall at finite Reynolds number, we extend the present methodology to an Oseen-like discrete Green's function. The extended discrete Green's function method is found to be accurate within at all wall-normal separations for particle Reynolds numbers up to 24. The discrete Green's function approach is well suited to dilute systems with significant mass loading and this is highlighted by comparison against other Euler–Lagrange as well as particle-resolved simulations of gas–solid turbulent channel flow. Strong particle–turbulence coupling is observed in the form of turbulence modification and turbophoresis suppression, and these observations are placed in context of the different methods.

Abstract: We introduce a variable selection procedure for function-on-function linear models with multiple functional predictors, using the functional principal component analysis (FPCA)-based estimation method with the group smoothly clipped absolute deviation regularization. This approach enables us to select significant functional predictors and estimate the bivariate functional coefficients simultaneously. A datadriven procedure is provided for choosing the tuning parameters of the proposed method to achieve high efficiency. We construct FPCA-based estimators for the bivariate functional coefficients using the proposed regularization method. Under some mild conditions, we establish the estimation and selection consistencies of the proposed procedure. Simulation studies are carried out to illustrate the finite-sample performance of the proposed method. The results show that our method is highly effective in identifying the relevant functional predictors and in estimating the bivariate functional coefficients. Furthermore, the proposed method is demonstrated in a real-data example by investigating the association between ocean temperature and several water variables.

Abstract: In this chapter, the recent results for the analysis of local fractional calculus are considered for the first time. The local fractional derivative (LFD) and the local fractional integral (LFI) in the fractional (real and complex) sets, the series and transforms involving the Mittag-Leffler function defined on Cantor sets are introduced and reviewed. The uniqueness of the solutions of the local fractional differential and integral equations and the local fractional inequalities are considered in detail. The local fractional vector calculus is applied to describe the Rice theory in fractal fracture mechanics.

Abstract: We prove and discuss several fixed point results for nonlinear operators, acting on some classes of functions with values in a b-metric space. Thus we generalize and extend a recent theorem of Dung and Hang (J Math Anal Appl 462:131–147, 2018), motivated by several outcomes in Ulam type stability. As a simple consequence we obtain, in particular, that approximate (in some sense) eigenvalues of some linear operators, acting in some function spaces, must be eigenvalues while approximate eigenvectors are close to eigenvectors with the same eigenvalue. Our results also provide some natural generalizations and extensions of the classical Banach Contraction Principle.

Abstract: In this paper, we consider the Sturm–Liouville problem with Dirichlet conditions in the case of time scales consists isolated points. Then, we obtain discrete Sturm–Liouville problem on a finite interval. We solve the inverse nodal problem, especially give a reconstruction formula for the potential function q.

Abstract: We consider the regression problem of estimating functions on $ \mathbb{R}^D $ but supported on a $ d $-dimensional manifold $ \mathcal{M} ~~\subset \mathbb{R}^D $ with $ d \ll D $. Drawing ideas from multi-resolution analysis and nonlinear approximation, we construct low-dimensional coordinates on $ \mathcal{M} $ at multiple scales, and perform multiscale regression by local polynomial fitting. We propose a data-driven wavelet thresholding scheme that automatically adapts to the unknown regularity of the function, allowing for efficient estimation of functions exhibiting nonuniform regularity at different locations and scales. We analyze the generalization error of our method by proving finite sample bounds in high probability on rich classes of priors. Our estimator attains optimal learning rates (up to logarithmic factors) as if the function was defined on a known Euclidean domain of dimension $ d $, instead of an unknown manifold embedded in $ \mathbb{R}^D $. The implemented algorithm has quasilinear complexity in the sample size, with constants linear in $ D $ and exponential in $ d $. Our work therefore establishes a new framework for regression on low-dimensional sets embedded in high dimensions, with fast implementation and strong theoretical guarantees.

Abstract: Efficient and accurate evaluation of free-surface Green's function is the key to hydrodynamic problems solved by boundary element method (BEM). However, so far, there is still no unified numerical method that can accurately approximate all kinds of free-surface Green's functions. In theory, machine learning can be used to approximate any function with high accuracy. In the present study, neural networks are used for the numerical approximation of pulsating source Green's function, and the corresponding optimization algorithms for gradient descent are adopted. Regularization is used to prevent overfitting. Double-precision numerical results obtained by Romberg quadrature are used as training set and validation set. To improve the accuracy of present numerical approximation, the calculation domain of both Green's function and its gradient are divided into 4 zones, and various network structures are adopted in each zone. Finally, a machine model, called ZeroGF, is obtained by machine learning that can predict Green's function and its derivatives. The numerical results show that ZeroGF owns at least 4 digits of accuracy in above 99% area of all zones. BEM program incorporating with ZeroGF is validated in the hydrodynamic calculation of the hemisphere, Wigley III and the Barge. Good accuracy and reliability of ZeroGF is shown.

Abstract: We present a learning method to learn the mapping from an input space to an action space, which is particularly suitable when the action is an optimal decision with respect to a certain unknown cost function. We use an inverse optimization approach to retrieve the cost function by introducing a new loss function and a new hypothesis class of mappings. A tractable convex reformulation of the learning problem is also presented. The method is effective for learning input-action mapping in continuous input-action space with input-output constraints, typically present in control systems. The learning approach can be effectively transformed to learn a Model Predictive Control (MPC) behaviour and a case study to mimic an MPC is presented, which is a rather computationally heavy control strategy. Simulation and experimental results show the effectiveness of the proposed approach.

Abstract: This work deals with Kumaraswamy distribution. Maximum likelihood, Bayes and expansion methods of estimation are used to estimate the reliability function. A symmetric Loss function (De-groot and NLINEX) are used to find the reliability function based on four types of informative prior three double priors and one single prior. In addition expansion methods (Bernstein polynomials and Power function) are applied to find reliability function numerically. Simulation research is conducted for the comparison of the effectiveness of the proposed estimators. Matlab (2015) will be used to obtain the numerical results.

Abstract: In view of the shortcomings and problems of the commonly used C language pseudo-random number generation function, the existing C language pseudo-random number generation function is improved, a general pseudo-random number generator based on chaos is proposed, and multiple types of random number acquisition interface are introduced. While improving the randomness of the original C language pseudo-random number function, the procedure of improvement also enhances its versatility and can better meet the needs of different types of random numbers. The test results of the pseudo-random number generator show that the random number generated by it has good randomness, and the function call is convenient and flexible.

Abstract: In this paper, we have introduced a new class of sets in topological space called $B^{ic}$-open set and we have introduced and study the properties of $B^{ic}$-continuous function and topological properties.