Interpolation Scheme Research Articles

Natural frequency analysis of bidirectional functionally graded plates based on a refined shear theory using nonconforming finite elements

In this study, we investigate the free vibrations of functionally graded plates using the refined shear deformation theory. These plates have material properties that vary in two directions: the z-axis and the x-axis. The variation follows a bidirectional gradation described by a power law based on the Voigt rule of mixture. The displacement field is characterized by four variables, chosen in such a way that shear strain and stress vanish, and traction is free at the upper and lower surfaces of the plate. This eliminates the need for a shear correction factor, as required in the first-order shear deformation theory. We derive the equations of motion and related boundary conditions using Hamilton’s principle and a finite element approach. The proposed element ensures C1-continuity by including the first derivative of the transverse deflection in the kinematic field. For spatial discretization, we employ a rectangular element with four nodes and two interpolation schemes. Lagrangian shape functions are used for in-plane displacement, while a Hermitian scheme is employed for transverse deflection. This enables us to determine the natural frequencies of the 2D-FG plates. We demonstrate the validity, convergence, and accuracy of the proposed element through illustrative examples and compare it with previous literature. Furthermore, we conduct numerical analyses to examine the effects of transverse shear function, boundary conditions, plate aspect ratio, slenderness ratio, and gradation indices on the natural frequencies. The developed model provides insights into the significance of natural frequencies in the design of various structural elements, including 2D-FG plates.

Multi-material structural discrete variable topology optimization with minimum length scale control under mass constraint

Topology optimization of multi-material structure has a larger design space compared with single material optimization, and it also requires efficient material selection methods to provide references for designers. The multi-material additive manufacturing ensures the manufacturability of topology design of multi-material structures, but also puts forward new manufacturability requirements, such as the minimum size of multi-material and the connection interface. This paper focuses on the development of a topology optimization algorithm for multi-material structures under the total mass constraint based on discrete variables. Recursive multiphase materials interpolation (RMMI) scheme is utilized to establish the relationship between discrete design variables and element elastic modulus and density, and sequential approximate integer programming (SAIP) and canonical relaxation algorithms are constructed to update the discrete design variables. It is shown that this method can obtain multi-material topology design with clear interface display, and has the ability of free material coexistence and degradation, which indicates that the discrete variables have more advantages than the continuous variables in the RMMI scheme. Based on this, the material selection under mass constraints is discussed in depth, and it is pointed out that the traditional determination of material coexistence or degradation in design based on specific stiffness has limitations. Thus, the present work expands the necessary conditions of material coexistence. Then, a minimum size control method of multi-material design based on post-processing is proposed to solve the minimum size geometry constraint problem by SAIP. Numerical examples show that this method can not only precisely control the minimum size of each material phase (including void phase), but also resolve the problem of interface and boundary diffusion of the multi-material phase, and significantly improve the manufacturability of multi-material design. Finally, it is applied to optimization problems with complex design domain and irregular meshes which are closer to engineering design.

Design of multi-material structures using material jetting technology: Topology optimisation, numerical analysis and experiments

This paper presents a thorough experimental/numerical validation of optimised multi-material structures fabricated by material jetting technology. The proposed methodology uses, on the one hand, non-uniform rational basis spline (NURBS) entities to represent the geometric descriptor associated with each material phase constituting the continuum and, on the other hand, a general multi-phase material interpolation scheme to penalise the stiffness tensor of the structure. Two design requirements are included in the problem formulation: the lightness and the minimum length scale of each material phase. The influence of the integer parameters intervening in the definition of the NURBS entity and the influence of different combinations of material phases on the optimised solutions are investigated. The proposed approach is applied to 2D and 3D benchmark structures subjected to prescribed displacements representative of a three-point bending test. Based on the result of the topology optimisation process one of the optimised solutions, balancing the requirements of structural stiffness, lightness, and manufacturing constraints, is selected, manufactured and tested. A comparison between experimental and numerical results (obtained by non-linear analyses) is carried out to show the effectiveness of the approach.

Reversible Data Hiding in Encrypted Images Based on Two-Round Image Interpolation

The data embedding of vacating room after encryption reversible data hiding in encrypted images (VRAE RDHEI) is performed on an encrypted image without redundancy and spatial correlation. Data extraction and image recovery rely on a range of unique mechanisms that utilize spatial correlation in the decrypted domain. Of these mechanisms, pixel prediction is among the most frequently used, directly affecting the capacity and fidelity. In this paper, we propose a novel method that uses a two-round interpolation mechanism to enhance pixel prediction precision while preserving a large number of carrier pixels. In the proposed method, the content owner uses a stream cipher to encrypt the image as a carrier. The data hider flips specific LSBs of the encrypted image for data embedding. On the receiver side, the process of data extraction and image recovery is divided into two stages. In each stage, based on the varying distributions of the original or recovered pixels with the carrier pixels, the corresponding pixel interpolation schemes are used to accurately predict the pixels for data extraction and image recovery. The results demonstrate that the proposed method can efficiently improve the capacity and fidelity with full reversibility compared to existing VRAE RDHEI methods.

Dynamic Topology Optimization of Constrained Layer Damping Structure Considering Non-Uniform Blast Load

The non-uniform distributed pressure of impact wave is usually simplified into concentrated or uniform load equivalently in the optimization design of constrained layer damping structure. However, for the thin-walled structure, it becomes necessary to regard the load as a non-uniform distribution. In this paper, a topology optimization approach is proposed considering the blast load with non-uniform distribution, aiming to unveil its impact on optimization layouts and dynamic responses. Initially, the smoothed particle hydrodynamics (SPH) algorithm is used to obtain the blast pressure what are extracted and integrated into the optimization model. Subsequently, the relative density is regarded as design variable. The construction of material penalty model and the topology optimization model are based on polynomial interpolation scheme (PIS). The sensitivity of objective function is deduced employing an improved adjoint variable method (AVM) to fit the load forms, and the Newmark-[Formula: see text] method is used to calculate the dynamic response. The optimization criterion (OC) is adopted to update the design variables. Finally, two numerical examples are used to exhibit the validity and accuracy of the presented methodology. The findings indicate that the distributed form, spread velocity, excited position and excited amplitude of the blast load all exert a notable influence on optimization results and dynamic response. These results underscore the valuable engineering application of this research and introduce a fresh perspective to the challenge of topology optimization under the blast case.

Node-bound communities for partition of unity interpolation on graphs

Graph signal processing benefits significantly from the direct and highly adaptable supplementary techniques offered by partition of unity methods (PUMs) on graphs. In our approach, we demonstrate the generation of a partition of unity solely based on the underlying graph structure, employing an algorithm that relies exclusively on centrality measures and modularity, without requiring the input of the number of subdomains. Subsequently, we integrate PUMs with a local graph basis function (GBF) approximation method to develop cost-effective global interpolation schemes. We also discuss numerical experiments conducted on both synthetic and real datasets to assess the performance of this presented technique.

A High-Precision Planar NURBS Interpolation System Based on Segmentation Method for Industrial Robot

NURBS curve parameter interpolation is extensively employed in precision trajectory tasks for industrial robots due to its smoother performance compared to traditional linear or circular interpolation methods. The trajectory planning systems for industrial robots necessitate four essential functional modules: first, the spline curve discretization technique ensuring chord error compliance; second, the contour scanning technique for determining the maximum feasible feed rate for multi-constraint and multi-segment paths; third, the technique for achieving a smooth feed rate profile; and fourth, the continuous curve parameter interpolation technique. Therefore, this paper proposes a high-precision planar NURBS interpolation system for industrial robots. Firstly, a segmentation method for NURBS curves based on a closed-loop chord error constraint is proposed, which segments the original global NURBS curve into a collection of Bezier curves that strictly meet the chord error constraint. Secondly, a bidirectional scanning technique is presented to meet the joint space constraint, establishing an analytical mapping between the tool tip kinematic constraint and the joint kinematic constraint. Then, based on the traditional S-shaped feed rate profile, an adaptive algorithm with a displacement constraint is introduced, considering the real-time speed adjustment requirements of robots. Finally, a compensation interpolation strategy based on arc length parameterization is adopted to solve the accumulated error problem in parameter interpolation. The effectiveness of and potential for enhancing the quality of planar machining of the proposed planar NURBS interpolation system for industrial robots are validated through simulations and experiments. The results demonstrate the system’s applicability and accuracy, and its ability to improve planar machining quality.

Innovative image interpolation based reversible data hiding for secure communication

In an increasingly digitized world, secure communication plays a pivotal role in safeguarding sensitive information and ensuring the confidentiality of data transmission. Conventional encryption techniques are robust but often result in a loss of data during transmission, making it challenging to achieve both security and image quality simultaneously. To address this issue, an innovative technique for interpolation-based reversible data hiding (RDH) within images is presented in this study. The interpolation scheme considers the Min function of the neighboring pixels and generates the cover image. After that, a data hiding method is applied to the cover image by considering the intensity range of the pixels using the least-significant bit (LSB) substitution method. The suggested embedding approach first encrypts the secret message, and then it encodes the encrypted secret message into interpolated pixels based on pixel intensity range groups. This technique enables the image to be consistently recreated once the data has been extracted. The percentage increment in the embedding capacity and peak signal-to-noise ratio (PSNR) is 40.00 and 8.64% for the proposed method as compared to the existing method. The experimental results indicate that the proposed technique enhanced the embedding capacity for all test pictures and produced higher PSNR values.

Degree reduction of ball Said‐Ball surfaces using an enhanced coot optimization algorithm

Degree reduction of ball Said‐Ball (BSB) surface is a complex and unsolved problem in computer‐aided design (CAD) and computer graphics (CG), which has potential application prospects in many engineering fields of geometric modeling. In this paper, the degree reduction of BSB surface is transformed into the optimization problems of center surface and radius function, and the improved coot optimization algorithm is used to solve it. An effective method to solve the degree reduction of BSB surface by intelligent optimization algorithm is proposed. Firstly, the degree reduction of BSB surface is decomposed into center surface reduction and radius function reduction, and the objective function and constraint conditions are determined according to the optimization idea, then the degree reduction optimization model of center surface and radius function is established. Secondly, an enhanced coot (ECOOT for short) algorithm is proposed, which combines chain strategy, dispersed foraging strategy, and quadratic interpolation strategy. Compared with the original algorithm, ECOOT algorithm has better convergence performance and higher calculation accuracy. Finally, the proposed ECOOT algorithm is used to solve the degree reduction optimization model, and the optimal center surface and minimum distance radius function of the BSB surface after degree reduction are obtained. The experimental results show that the ECOOT algorithm can effectively solve the problem of degree reduction for BSB surface, the degree reduction error is reduced by about 20% compared to the original COOT algorithm, and is superior to other intelligent optimization algorithms in accuracy, robustness, and convergence.

Multipopulation-Based Differential Evolution for Large-Scale Many-Objective Optimization.

In recent years, numerous efficient many-objective optimization evolutionary algorithms have been proposed to find well-converged and well-distributed nondominated optimal solutions. However, their scalability performance may deteriorate drastically to solve large-scale many-objective optimization problems (LSMaOPs). Encountering high-dimensional solution space with more than 100 decision variables, some of them may lose diversity and trap into local optima, while others may achieve poor convergence performance. This article proposes a multipopulation-based differential evolution algorithm, called LSMaODE, which can solve LSMaOPs efficiently and effectively. In order to exploit and explore the exponential decision space, the proposed algorithm divides the population into two groups of subpopulations, which are optimized with different strategies. First, the randomized coordinate descent technique is applied to 10% of individuals to exploit the decision variables independently. This subpopulation maintains diversity in the decision space to avoid premature convergence into local optimum. Second, the remaining 90% of individuals are optimized with the nondominated guided random interpolation strategy, which interpolates individual among three nondominated solutions randomly. The strategy can guide the population convergent toward the nondominated solutions quickly, meanwhile, maintain good distribution in objective space. Finally, the proposed LSMaODE is evaluated on the LSMOP test suites from the scalability in both decision and objective dimensions. The performance is compared against five state-of-the-art large-scale many-objective evolutionary algorithms. The experimental results show that LSMaODE provides highly competitive performance.

On the multi-objective perspective of discrete topology optimization in fluid-structure interaction problems

Fluid-structure interaction is a challenging topic that addresses fluid and solid physics, as well as the stress coupling between them. Traditional topology optimization methods are performed with coupling load interpolation schemes in order to have some information on fluid flow sensitivity analysis and improve compliance minimization into design-dependent problems. In this paper, we propose a strategy to design fluid-structure systems by combining solid and fluid objectives without the need for the coupling load interpolation scheme. Therefore, we investigate topology optimization applied to fluid-structure interaction problems via a multi-objective formulation. We combine structural compliance with some fluid flow objective functions (energy dissipation, downforce, and drag) subject to a volume constraint. We assume linear elasticity for the solid and the steady incompressible Navier-Stokes equations for the fluid. The optimization problem is solved by using sequential integer linear programming via the TOBS (Topology Optimization of Binary Structures) method with a geometry trimming (GT) technique. It is a gradient-based method that produces explicit (discrete) boundaries that are convenient for coupled physics problems. Numerical results are presented for two and three-dimensional problems considering low to moderate Reynolds numbers. We demonstrate that the proposed multi-objective approach yields physically meaningful designs with improved performance, highlighting that the incorporation of coupling load interpolation into fluid-structure interaction optimization is redundant.

Free surface tension modelling using particle-grid hybrid method without considering gas particles

In particle method, the number of particles is an important factor affecting the computational efficiency. For the free-surface flow, gas particles are usually necessary for surface tension calculation, but they have little impact to the liquid flow with a large density. If the free surface tension can be modeled without considering gas particles, it is of great significance to improve the computational efficiency. The existing potential-based surface tension model and the stress-based surface tension model don't need gas particles, but they have the problems of instability or are difficult to implement. Therefore, in this study, another free surface tension calculation method by coupling particles and grids is proposed. In the method, background mesh is applied to the entire computational domain, where the boundary of grid is extended outward by 3.1 l0 on the basis of particle boundary for curvature calculation. The level-set function is adopted to mark different phases on background mesh, whose value is set according to the normal distance between the grid and the nearest free surface particle. After obtaining the curvature and the level-set function gradient on background mesh, these variables are interpolated to corresponding particles to calculate the surface tension. Through the comparative study of area-weighted and distance-weighted interpolation schemes, it is found that the accuracy of distance-weighted interpolation scheme is higher than that of area-weighted interpolation scheme. Finally, the performance of the particle-grid hybrid method is verified by simulating four benchmark cases, including droplet oscillations, droplet spreading, capillary jet breakup and droplet impinging onto plate.

Topology Optimization of Geometrically Nonlinear Structures Based on a Self-Adaptive Material Interpolation Scheme

Topology Optimization of Geometrically Nonlinear Structures Based on a Self-Adaptive Material Interpolation Scheme

Advanced finite element analyses to compute the J-integral for delaminated composite plates

This study concerns several possible ways of computing the J-integral in the cases of laminated composite plate structures containing delamination. On the one hand, two special types of plate finite element families are provided for advanced fracture mechanical analyses. On the other hand, a numerical algorithm is introduced also that utilizes results derived from solid element models built in commercial finite element software. In order to create plate elements, the first-order (FSDT) and the third-order shear deformation theories (TSDT) are applied with two equivalent single layers. A subparametric element family is created by using the Hermite interpolation with the FSDT that grants the continuity of the derivatives at the element borders. Besides, an isoparametric family is created also with the TSDT employing the Lagrange interpolation scheme. This paper aims to present the compact formulations of calculating the J-integral around the delamination fronts for both plate element families. The calculation of the J-integral is implemented as a matrix multiplication by taking advantage of the properties of plate transition elements. The models yield improved fracture mechanical analyses for delaminated composite plates with linear elastic material behaviour. In addition, computing the J-integral is not available in most commercial finite element software for laminated orthotropic materials, therefore, an appropriate algorithm is presented here for this purpose. To conclude, the models and algorithms are applied to two case studies and subjected to a comprehensive comparison with analytical and formerly used finite element solutions.

Soft- and Hard-Kill Hybrid GPU-based Bi-Directional Evolutionary Structural Optimization

Abstract Bi-directional evolutionary structural optimization (BESO) is a well-recognized method for generating optimal topologies of structures. Its soft-kill variant has a high computational cost, especially for large-scale structures, whereas the hard-kill variant often faces convergence issues. Addressing these issues, this paper proposes a hybrid BESO model tailored for graphics processing units (GPUs) by combining the soft-kill and hard-kill approaches for large-scale structures. A GPU-based algorithm is presented for dynamically isolating the solid/hard elements from the void/soft elements in the finite element analysis (FEA) stage. The hard-kill approach is used in the FEA stage with an assembly-free solver to facilitate the use of high-resolution meshes without exceeding the GPU memory limits, whereas for the rest of the optimization procedure, the soft-kill approach with a material interpolation scheme is implemented. Furthermore, the entire BESO method pipeline is accelerated for both the proposed hybrid and the standard soft-kill BESO. The comparison of the hybrid BESO with the GPU-accelerated soft-kill BESO using four benchmark problems with more than two million degrees of freedom reveals three key benefits of the proposed hybrid model: reduced execution time, decreased memory consumption, and improved FEA convergence, all of which mitigate the major computational issues associated with BESO.

Multiscale topology optimization framework for natural frequency maximization of multi-morphology lattice structures

This paper proposes a multiscale topology optimization framework for maximizing the natural frequency of multi-morphology lattice structures (MMLSs). The proposed framework addresses the challenges of computational efficiency, design space, numerical convergence, and compatibility between adjacent microstructures in multiscale topology optimization for natural frequency problems. The macrostructural topology, the morphologies categories, distribution regions, volume fractions of different lattice materials (LMs), and the relative densities of lattice unit cells (LUCs) are simultaneously optimized to enhance the structural natural frequency. Specifically, level set functions are utilized to generate prototype LUCs, enabling obtaining graded LUCs by configuration interpolation. Multi-morphology LUCs with smooth characteristics are achieved using the Kriging-assisted morphological post-process and sigmoid function (SF) based hybrid transition strategy. Kriging metamodel-assisted Uniform Multiphase Materials Interpolation (KUMMI) schemes are constructed for the mechanical properties estimation of macro elements to promote the multiscale topology optimization procedure. Distinct processing methods for LUCs' elasticity tensor and density are incorporated within the KUMMI schemes, by which the local mode problem is avoided. The objective order natural frequency is accurately optimized with the modal assurance criterion (MAC) based mode-tracking technique. Numerical examples demonstrate that the developed design framework can efficiently maximize the natural frequency of MMLSs, while also ensuring microstructural connectivity.

Construction of [formula omitted] spatial interpolants with prescribed arc lengths

In this paper the problem of constructing spatial G2 continuous Pythagorean-hodograph (PH) spline curves, that interpolate points and frame data, and in addition have the prescribed arc-length, is addressed. The interpolation scheme is completely local and can be directly applied for motion design applications. Each spline segment is defined as a PH biarc curve of degree 7 satisfying super-smoothness conditions at the biarc’s joint point. The biarc is expressed in a closed form with additional free parameters, where one of them is determined by the length constraint. The selection of the remaining free parameters is suggested, that allows the existence of the solution of the length interpolation equation for any prescribed length and any ratio between norms of boundary tangents. By the proposed automatic procedure for computing the frame and velocity quaternions from the first and second order derivative vectors, the paper presents a direct generalization of the construction done for planar curves to spatial ones. Several numerical examples are provided to illustrate the proposed method and to show its good performance, also when a spline construction in considered.

CHEX-MATE: A non-parametric deep learning technique to deproject and deconvolve galaxy cluster X-ray temperature profiles

Temperature profiles of the hot galaxy cluster intracluster medium (ICM) have a complex non-linear structure that traditional parametric modelling may fail to fully approximate. For this study, we made use of neural networks, for the first time, to construct a data-driven non-parametric model of ICM temperature profiles. A new deconvolution algorithm was then introduced to uncover the true (3D) temperature profiles from the observed projected (2D) temperature profiles. An auto-encoder-inspired neural network was first trained by learning a non-linear interpolatory scheme to build the underlying model of 3D temperature profiles in the radial range of [0.02–2] R500, using a sparse set of hydrodynamical simulations from the THREE HUNDRED PROJECT. A deconvolution algorithm using a learning-based regularisation scheme was then developed. The model was tested using high and low resolution input temperature profiles, such as those expected from simulations and observations, respectively. We find that the proposed deconvolution and deprojection algorithm is robust with respect to the quality of the data, the morphology of the cluster, and the deprojection scheme used. The algorithm can recover unbiased 3D radial temperature profiles with a precision of around 5% over most of the fitting range. We apply the method to the first sample of temperature profiles obtained with XMM-Newton for the CHEX-MATE project and compared it to parametric deprojection and deconvolution techniques. Our work sets the stage for future studies that focus on the deconvolution of the thermal profiles (temperature, density, pressure) of the ICM and the dark matter profiles in galaxy clusters, using deep learning techniques in conjunction with X-ray, Sunyaev Zel’Dovich (SZ) and optical datasets.

Water level recognition based on deep learning and character interpolation strategy for stained water gauge

AbstractDue to the diversity of climate and environment in China, the frequent occurrence of extreme rainfall events has brought great challenges to flood prevention. Water level measurement is one of the important research topics of flood prevention. Recently, the image‐based water level recognition method has become an important part of water level measurement research due to its advantages in easy installation, low cost, and zero need of manual reading. However, there are two mainly shortcomings of the existing image‐based water level recognition methods: (1) severely affected by light intensity and (2) low accuracy of water level recognition for stained water gauges. To solve these two problems, this paper proposes a water level recognition method in consideration of complex scenarios. This method first uses a semantic segmentation convolutional neural network to extract the water gauge mask, and then uses the YOLOv5 object detection network to extract the letter “E” on the water gauge. Based on the character sequence inspection strategy, the algorithm dynamically compensates for the missed detection of characters of stained water gauges. Through a large number of experiments, the proposed water level measurement method has good robustness in complex scenarios, meeting the needs of flash flood defense.

A black-box optimization strategy for customizable global elastic deformation behavior of unit cell-based tri-anti-chiral metamaterials

Metamaterials are a class of materials with a distinctive unit cell-based periodic architecture, often resulting in unique mechanical properties. The potential of metamaterials can be further improved by using gradients of unit cell parameters and thereby creating a specific distribution of material properties in a part. This design freedom comes with the challenge of finding new suitable design and optimization strategies. In this study, a Finite Element simulation-based optimization framework to create a predefined deformation behavior of unit cell-based metamaterials is presented. The framework consists of a numerical homogenization method to create an efficient linear elastic material representation, an interpolation scheme for a fast correlation of unit cell parameters with homogenized material properties and a black-box based optimization part. The framework is tested on a tri-anti-chiral metamaterial with additional geometric parameters and a newly developed transition unit cell. Several specified lateral deformations of simple 2D rectangular test specimens under tensile load are used as primary optimization targets and the homogenized results are validated against Finite Element simulations of fully modeled tri-anti-chiral structures. In addition, the effect of the design space and the number of design variables is studied and several combinations of optimization objectives and constraints are tested.