Michael Yu Wang

Bio: Michael Yu Wang is an academic researcher from Hong Kong University of Science and Technology. The author has contributed to research in topics: Topology optimization & Level set method. The author has an hindex of 52, co-authored 333 publications receiving 11087 citations. Previous affiliations of Michael Yu Wang include The Chinese University of Hong Kong & Dalian University of Technology.

Energy absorption characteristics of metallic triply periodic minimal surface sheet structures under compressive loading

Abstract: Designing metallic cellular structures with triply periodic minimal surface (TPMS) sheet cores is a novel approach for lightweight and multi-functional structural applications. Different from current honeycombs and lattices, TPMS sheet structures are composed of continuous and smooth shells, allowing for large surface areas and continuous internal channels. In this paper, we investigate the mechanical properties and energy absorption abilities of three types of TPMS sheet structures (Primitive, Diamond, and Gyroid) fabricated by selective laser melting (SLM) with 316 L stainless steel under compression loading and classify their failure mechanisms and printing accuracy with the help of numerical analysis. Experimental results reveal the superior stiffness, plateau stress and energy absorption ability of TPMS sheet structures compared to body-centred cubic lattices, with Diamond-type sheet structures performing best. Nonlinear finite element simulation results also show that Diamond and Gyroid sheet structures display relatively uniform stress distributions across all lattice cells under compression, leading to stable collapse mechanisms and desired energy absorption performance. In contrast, Primitive-type structures display rapid diagonal shear band development followed by localized wall buckling. Lastly, an energy absorption diagram is developed to facilitate a systematic way to select optimal densities of TPMS structures for energy absorbing applications.

Radial basis functions and level set method for structural topology optimization

Abstract: Level set methods have become an attractive design tool in shape and topology optimization for obtaining lighter and more efficient structures. In this paper, the popular radial basis functions (RBFs) in scattered data fitting and function approximation are incorporated into the conventional level set methods to construct a more efficient approach for structural topology optimization. RBF implicit modelling with multiquadric (MQ) splines is developed to define the implicit level set function with a high level of accuracy and smoothness. A RBF-level set optimization method is proposed to transform the Hamilton-Jacobi partial differential equation (PDE) into a system of ordinary differential equations (ODEs) over the entire design domain by using a collocation formulation of the method of lines. With the mathematical convenience, the original time dependent initial value problem is changed to an interpolation problem for the initial values of the generalized expansion coefficients. A physically meaningful and efficient extension velocity method is presented to avoid possible problems without reinitialization in the level set methods. The proposed method is implemented in the framework of minimum compliance design that has been extensively studied in topology optimization and its efficiency and accuracy over the conventional level set methods are highlighted. Numerical examples show the success of the present RBF-level set method in the accuracy, convergence speed and insensitivity to initial designs in topology optimization of two-dimensional (2D) structures. It is suggested that the introduction of the radial basis functions to the level set methods can be promising in structural topology optimization.

A level set-based parameterization method for structural shape and topology optimization

Abstract: This paper presents an effective parametric approach by extending the conventional level set method to structural shape and topology optimization using the compactly supported radial basis functions (RBFs) and the optimality criteria (OC) method. The structural design boundary is first represented implicitly by embedding into a higher-dimensional level set function as its zero level set, and the RBFs of a favorable smoothness are then applied to interpolate the level set function. The original initial value problem is thus converted to a parametric optimization, with the expansion coefficients of the interplant posed as the design variables. The OC method is then applied to advance the structure boundary in terms of the velocity field derived from the parametric optimization. Hence, the structural shape and topology optimization is now transformed into a process of iteratively finding coefficients to update the level set function to achieve an optimal configuration. The numerical considerations of the conventional level set method, including upwind schemes, velocity extension, and reinitialization, are eliminated. The proposed scheme is capable of addressing structural shape fidelity and topology change simultaneously and of keeping the boundary smooth during the optimization process. Furthermore, numerical convergence is expected to be improved. A widely investigated example, in the framework of structural stiffness designs, is applied to demonstrate the efficiency and accuracy of the proposed approach. Copyright © 2007 John Wiley & Sons, Ltd.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Coverage control for mobile sensing networks

Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Topology optimization approaches: A comparative review

Abstract: Topology optimization has undergone a tremendous development since its introduction in the seminal paper by Bendsoe and Kikuchi in 1988. By now, the concept is developing in many different directions, including “density”, “level set”, “topological derivative”, “phase field”, “evolutionary” and several others. The paper gives an overview, comparison and critical review of the different approaches, their strengths, weaknesses, similarities and dissimilarities and suggests guidelines for future research.