About

Shikui Chen is a Professor in the Department of Mechanical Engineering at Stony Brook University. He earned his Ph.D. in 2010 from Northwestern University and his earlier degree in 2006 from the Chinese University of Hong Kong, specializing in Automation and Computer Aided Engineering. His research focuses on various aspects of mechanical engineering, contributing to the understanding and development of automation, computational engineering, and related fields. His work involves applying advanced engineering principles to solve complex problems in mechanical systems, although specific research areas are not detailed on the page.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Engineering
• Mathematical optimization
• Geometry
• Structural engineering
• Algorithm
• Applied mathematics
• Electronic engineering
• Electrical engineering
• Mathematical analysis
• Physics
• Mechanical engineering

Selected publications

• Multi-Material Topology and Magnetization Co-Optimization of Circular Halbach Arrays Via a Cardinal Basis Function-Based Level Set Method

  Journal of Mechanical Design · 2026-02-06

  articleSenior author

  Abstract A Halbach array is a specialized arrangement of permanent magnets that generates a strong, uniform magnetic field in a designated region and suppresses it elsewhere. This configuration has been widely applied in magnetic levitation systems, electric motors, particle accelerators, and magnetic resonance imaging devices due to its high efficiency, reduced weight, and precise directional control. Linear Halbach arrays concentrate the field on one side, making them suitable for applications such as maglev trains and conveyor systems. Cylindrical Halbach arrays, with magnets arranged in a circular configuration, produce a uniform internal field while minimizing the external field, which is advantageous in brushless motors and imaging systems. The traditional design of Halbach arrays has relied heavily on engineering intuition because of the complexity of magnet placement and orientation. With advances in numerical methods, topology optimization now provides a systematic approach to determining both material distribution and magnetization directions to maximize magnetic flux in the target region. In this article, we propose a cardinal basis function (CBF)-based level set method for the design of circular Halbach arrays capable of generating an area-averaged magnetic field. The finite difference method is employed to optimize magnetization directions simultaneously with geometry, providing additional design flexibility. The CBF-based level set method reduces computational cost and accelerates convergence, improving the overall efficiency of the optimization. Furthermore, multi-material topology optimization is incorporated, enabling the addition of permanent magnets with distinct magnetization directions to achieve greater control over magnetic flux distribution.

  PublisherDOI

• Aneurysm Morphology Based on Conformal Geometry

  International Journal for Numerical Methods in Biomedical Engineering · 2026-05-01

  article

  Several morphological parameters such as aortic neck length, angulation, or centerline curvature have previously been evaluated to define "hostile" anatomies that predispose to poor outcomes after endovascular aneurysm repair. In this study, we present a new method for classifying aneurysm morphologies using their conformal structures. The conformal structure of a surface is determined by its Riemannian metric, and conformal mappings between surfaces with complex topologies preserve their conformal structures. To classify aneurysm shapes, the aortic aneurysm is first segmented from CT scans and represented as a discrete surface. Next, holomorphic differential forms are computed based on the discrete Hodge Theory. The aneurysm surface can be conformally mapped onto a pair of planar rectangles with an aortic bifurcation point by integrating a special holomorphic differential. This canonical configuration gives the conformal invariants, or "conformal fingerprints," which can then be used to classify aneurysm morphologies. This novel methodology shows promise in providing an improved understanding of aneurysm morphologies, which can aid in better predicting and managing potential complications after endovascular aneurysm repair.

  PublisherDOI

• Shape and Topology Optimization of Circular Halbach Array Using a Cardinal Basis Function (CBF) Based Parametric Level Set Method

  2025-08-17 · 1 citations

  articleSenior author

  Abstract A Halbach array is a specialized arrangement of permanent magnets designed to generate a strong, uniform magnetic field in the designated region. This unique configuration has been widely utilized in various applications, including magnetic levitation (maglev) systems, electric motors, particle accelerators, and magnetic seals. The advantages of Halbach arrays include high efficiency, reduced weight, and precise directional control of the magnetic field. Halbach arrays are commonly categorized into two configurations: linear and cylindrical. A linear Halbach array produces a concentrated magnetic field on one face and is frequently employed in maglev trains and conveyor systems to ensure stable and efficient operation. In contrast, a cylindrical Halbach array consists of magnets arranged in a ring, generating a uniform magnetic field within the cylinder while suppressing the external field. This configuration is particularly advantageous in applications such as brushless electric motors and magnetic resonance imaging (MRI) systems. Traditionally, the design of electromagnetic systems incorporating Halbach arrays relied on engineers’ expertise and intuition due to the complexity of the permanent magnet configuration. However, advancements in numerical methods, particularly topology optimization, have introduced a systematic approach to optimizing the shape and distribution of permanent magnets within a given design domain. In the context of Halbach array design, topology optimization aims to maximize the total magnetic flux within a designated region while simultaneously determining the optimal material distribution to achieve a specified design objective. This approach enhances the performance and efficiency of Halbach arrays, providing a more precise and automated framework for their development. In this paper, we propose a Cardinal Basis Function (CBF)-based level-set method for designing a circular Halbach array capable of generating a uniform magnetic field within a designated region. The CBF-based level-set method offers significant computational advantages by reducing the computational cost and accelerating the convergence process. This approach enhances the efficiency of the optimization process, making it a promising technique for the systematic design of Halbach arrays.

  PublisherDOI

• Tuning of ILADRC for CFB Boiler Combustion System Based on LF-DCSSA Algorithm

  Energies · 2025-04-23

  articleOpen access

  Aiming at the problem that it is difficult to adjust the parameters of the controller in the circulating fluidized bed (CFB) boiler combustion system due to its multivariable and strong coupling, an improved linear active disturbance rejection controller (ILADRC) parameter tuning strategy based on the Lévy flight double chaotic sparrow search algorithm (LF-DCSSA) is proposed. The LF-DCSSA algorithm is used to tune the parameters of the ILADRC controller in the multivariable coupled combustion control system of the CFB boiler built by Simulink, so that its control effect can reach the best state. The step response simulation and perturbation simulation are carried out with the theoretically tuned PID and ILADRC. The simulation results show that LF-DCSSA-ILADRC has obvious advantages in the three indexes of time–domain response, such as adjustment time, overshoot, and ITAE, which is more efficient and accurate than that of the theoretical setting, providing a new strategy for the control of the CFB boiler combustion system.

  PublisherOA PDFDOI

• 536 Topology optimization for personalized intracranial aneurysm implant design

  Journal of Clinical and Translational Science · 2025-03-25

  articleOpen access

  Objectives/Goals: To develop a personalized computational framework integrating computational fluid dynamics (CFD) and topology optimization for designing intracranial aneurysm implants. The primary objective is to reduce intra-aneurysmal blood flow velocity and enhance thrombus formation for improved treatment outcomes. Methods/Study Population: Patient-specific aneurysm geometries were extracted from pre-treatment rotational angiograms. A CFD-driven topology optimization framework was employed to design implants that reduce intra-aneurysmal flow velocity. The fluid dynamics were modeled using Navier–Stokes equations and the structural integrity of the implants was ensured by linear elasticity equations. The solid isotropic material with penalization (SIMP) method was applied to optimize the implant’s porous architecture, balancing flow reduction with structural support. COMSOL Multiphysics software was used to implement the optimization. Results/Anticipated Results: The optimized implants demonstrated significant reductions in intra-aneurysmal blood flow velocity and improved hemodynamic conditions. Flow velocity within the aneurysm was reduced by 77%, and the fluid energy dissipation ratio showed a 78.9% improvement compared to pretreatment conditions. The optimized porous structures were tailored to the aneurysm’s specific geometry, providing personalized designs that improve flow stasis and thrombus formation. Further validation of the implants will be performed in vitro and in vivo to assess their effectiveness and biocompatibility. Discussion/Significance of Impact: This personalized implant design framework could lead to better treatment outcomes by reducing aneurysm recurrence and complications compared to current devices. It provides a pathway for improved occlusion rates and patient-specific solutions for intracranial aneurysms.

  PublisherOA PDFDOI

• Level-Set Nonlinear Topology Optimization for Large-Deformation Compliant Mechanisms With Hyperelastic Materials

  2025-08-17

  articleSenior author

  Abstract The level set method has been widely applied in topology optimization of mechanical structures, primarily for linear materials, but its application to nonlinear hyperelastic materials, particularly for compliant mechanisms, remains largely unexplored. This paper addresses this gap by developing a comprehensive level set-based topology optimization framework specifically for designing compliant mechanisms using neo-Hookean hyperelastic materials. A key advantage of hyperelastic materials is their ability to undergo large, reversible deformations, making them well-suited for soft robotics and biomedical applications. However, existing nonlinear topology optimization studies using the level set method mainly focus on stiffness optimization and often rely on linear results as preliminary approximations. Our framework rigorously derives the shape sensitivity analysis using the adjoint method, including crucial higher-order displacement gradient terms often neglected in simplified approaches. By retaining these terms, we achieve more accurate boundary evolution during optimization, leading to improved convergence behavior and more effective structural designs. The proposed approach is first validated with a mean compliance problem as a benchmark, demonstrating its ability to generate optimized structural configurations while addressing the nonlinear behavior of hyperelastic materials. Subsequently, we extend the method to design a displacement inverter compliant mechanism that fully exploits the advantages of hyperelastic materials in achieving controlled large deformations. The resulting designs feature smooth boundaries and clear structural features that effectively leverage the material’s nonlinear properties. This work provides a robust foundation for designing advanced compliant mechanisms with large deformation capabilities, extending the reach of topology optimization into new application domains where traditional linear approaches are insufficient. The developed methodology is expected to provide a timely solution to computational design for soft robotics, flexible mechanisms, and other emerging technologies that benefit from hyperelastic material properties.

  PublisherDOI

• Geometry-Driven Design of Morphable Surface Structures Using Topology Optimization and Circle Packing

  Journal of Mechanical Design · 2025-02-13 · 2 citations

  articleSenior author

  Abstract This paper introduces a new computational framework for modeling and designing morphable surface structures based on an integrated approach that leverages circle packing for surface representation, conformal mapping to link local and global kinematics, and topology optimization for actuator design. The framework utilizes a unique strategy for employing optimized compliant actuators as the basic building blocks of the morphable surface. These actuators, designed as circular elements capable of modifying their radius and curvature, are optimized using level set topology optimization, considering both kinematic performance and structural stiffness. Circle packing is employed to represent the surface geometry, while conformal mapping guides the kinematic analysis, ensuring alignment between local actuator motions and desired global surface transformations. The design process involves mapping optimized component designs back onto the circle packing representation, facilitating coordinated control, and achieving harmony between local and global geometries. This leads to efficient actuation and enables precise control over the surface morphology. The effectiveness of the proposed framework is demonstrated through two numerical examples, showcasing its capability to design complex, morphable surfaces with potential applications in fields requiring dynamic shape adaptation.

  PublisherDOI

• Accelerating Electric-Magnetic Machine Simulation Using the Fourier Neural Operator (FNO)

  2025-08-17

  articleSenior author

  Abstract Electrical machines traditionally rely on Finite Element Analysis (FEA) to evaluate or simulate their properties by solving the associated partial differential equations (PDEs). However, FEA is computationally costly, which limits its capability for rapid design iteration and real-time simulations. While recent surrogate models such as Physics-Informed Neural Networks (PINNs) have shown promise, they often suffer from slow convergence and scalability issues in complex geometries. In this paper, we propose the use of the Fourier Neural Operator (FNO) as a resolution-invariant surrogate model to significantly reduce the computation time required for FEA-based PDE solutions in electric machines. Previous research has demonstrated the FNO’s ability to learn mappings for time-sequence problems by approximating operators between function spaces. Building on this, we present a methodology to directly predict the later-state electromagnetic fields of a rotating interior permanent magnet (IPM) motor based on its earlier-stage data by approximating the underlying operator that governs these transitions. Our framework enables full-geometry modeling without relying on segmentation, preserving accuracy while dramatically improving computational efficiency. The model was trained and validated on an FEA dataset with multiple boundary conditions and motor configurations, demonstrating strong generalization across different designs and resolutions. Experimental results show that the proposed FNO method achieves a significant reduction in computational time compared to traditional FEA simulations while maintaining an acceptable level of accuracy. This study highlights the potential of neural operators for accelerating electromagnetic simulations, enabling faster design iterations and offering new possibilities for real-time and optimization-based applications in electric machine design.

  PublisherDOI

• Robust Surface Remeshing Based on Conformal Welding

  Society for Industrial and Applied Mathematics eBooks · 2024-01-01

  book-chapter

  This work proposes a novel algorithm to eliminate the self-overlapping based on conformal welding theory, to improve the robustness of the surface remeshing algorithm. The proposed algorithm constructs a planar annulus with a high-quality triangulation, then weld the input surface with the annulus along their corresponding boundary components. The welded surface has a well-defined Riemannian metric and can be conformally mapped onto a convex planar domain by using the dynamic discrete surface Ricci flow method. This method guarantees the conformal mapping restricted on the input surface is a global embedding, and resolves the problem of self-intersections in the neighborhood of the boundary. The conformal welding method has been tested to remesh real car models. The experimental results demonstrate the method is highly effective in practice and greatly improves the robustness of the remeshing algorithm based on conformal uniformization.

  PublisherDOI

• Optimal Surface Quadrilateral Mesh Generation

  Society for Industrial and Applied Mathematics eBooks · 2024-01-01

  book-chapter

  Structured mesh generation has fundamental importance, but controlling the qualities of structured meshes remains a challenge. This work proposes a rigorous and practical algorithm to generate quadrilateral meshes on topological polyannuli with the least number of singularities. It is shown that each quad-mesh with 4k vertex degree induces a holomorphic differential. All the holomorphic differentials form a finite-dimensional linear space. A geometric distortion energy is proposed to measure the local area distortion. One can achieve quad-meshes as uniformly as possible by optimizing the distortion energy in the linear space. Experimental results show the proposed algorithm is able to improve the uniformity of the quad-meshes, and the method has the potential to be generalized to handle quad-meshes with other constraints.

  PublisherDOI

Recent grants

• Collaborative Research: Computational Framework for Designing Conformal Stretchable Electronics

  NSF · $415k · 2018–2022

• A New Level-Set-Based Robust Topology Optimization Approach with Applications to Design of Phononic Metamaterials

  NSF · $393k · 2015–2019

Frequent coauthors

• Xianfeng Gu

  Stony Brook University

  103 shared

• Jiang Long

  38 shared

• Qian Ye

  Lanzhou Institute of Chemical Physics

  36 shared

• Xiaoqiang Xu

  State University of New York

  34 shared

• Jiawei Tian

  Stony Brook University

  29 shared

• Panagiotis Vogiatzis

  State University of New York

  29 shared

• Yang Guo

  Zhengzhou University

  20 shared

• Na Lei

  18 shared

Education

• Ph.D., Mechanical Engineering

  Stony Brook University

  2000

• M.S., Mechanical Engineering

  Stony Brook University

  1997

• B.S., Mechanical Engineering

  Tsinghua University

  1994

Awards & honors

• ASME Compliant Mechanisms Theory Award in the ASME 31st Mech…