Mesh Deformation Research Articles

Course control in a self-consistent model of cuttlefish movement

We developed a simulation model to mimic cuttlefish movement. We developed a simulation model to mimic cuttlefish movement, representing an elongated body with two undulatory fins that generate propulsive forces for underwater movement. Our mathematical model concurrently solved equations for both body mechanics and fluid dynamics, using the Navier–Stokes equations to describe the latter. To implement this self-consistent model, we utilized deformable mesh techniques. This enabled us to compute both the apparatus’s movement performance characteristics and hydrodynamic flow parameters, such as vorticity and pressure fields. Our study focused on examining how oscillations of the left and right fins, each with different parameters, impact the apparatus’s maneuverability. We found that differences in frequencies between the left and right fins resulted in a peak turning angle velocity. We also explored how the interplay between hydrodynamic forces influences the apparatus’s course control.

Modeling Method of Meshing Stiffness of High Speed Spur Gears

As one of the most important dynamic excitation sources, the gear time-varying mesh stiffness is the key parameter of gear system dynamic model. So how to calculate the gear mesh stiffness accurately is of great importance. Based on Euler beam theory, this paper proposes a primitive calculation algorithm (PCA) to calculate the time-varying mesh stiffness of spur gears. A simplified gear model is developed based on nonlinear strain-displacement relationships, and a finite element program is used to simulate the gear's meshing stiffness. The rationality of this method was verified through finite element analysis. The results indicate that mesh stiffness plays a crucial role in controlling mesh deformation. In view of this, this study lays a theoretical foundation for further analysis of the vibration and noise of gears.

Multi-body mesh deformation using a multi-level localized dual-restricted radial basis function interpolation

Multi-body dynamic problems with moving boundaries are prevalent in the computational fluid dynamics. In these problems, the original mesh is unsuitable for subsequent solution steps and is needed to be deformed. The radial basis function (RBF) interpolation is a common algorithm for the mesh deformation task. However, the multi-body mesh may contain numerous individuals and volume nodes, which will harm the computational efficiency. In order to optimize this process, we propose a new method for the mesh deformation of the multi-body configuration. In this new approach, each individual is deformed separately. Thus the global problem is decomposed into a series of local mesh deformation problems. As the computational cost to construct the interpolation system in RBF algorithm is proportional to the cube of the number of support points on the surface, converting it into multiple local mesh deformation problems can effectively reduce the CPU cost. To treat each local problem, we employ a dual-restricted RBF interpolation technique which could avoid the influence of moving individual on the other individuals. This new localized approach effectively improves the computational efficiency to construct the interpolation system but sometimes will increase the CPU cost of the mesh updating procedure. To avoid this drawback, the existed multi-level restricted RBF strategy is coupled with the new localized method to further reduce the CPU cost to update the mesh. The combination of the two techniques could enhance their advantages and avoid their drawbacks. Some numerical examples have demonstrated the abilities of the new algorithm. For instance, in the case of the three-dimensional birds flock, the CPU time to construct the interpolation system and deform the volume mesh was respectively reduced by three and two orders of magnitude compared to the global single-level method.

Application of B-spline basis functions as harmonic functions for the concurrent shape and mesh morphing of airfoils

The automatic deformation of the computational mesh along with the deformed geometry in design optimization cycles is a valuable procedure, as it reduces the required time for the construction of new meshes. The introduction of harmonic coordinates for the deformation of objects included within a closed mesh (cage) has been introduced in computer graphics. Harmonic coordinates result from solutions to the Laplace’s equation (harmonic functions) using a numerical solver. In this work, a modification to the classical harmonic coordinates’ concept is introduced for the deformation of 2D geometries (and the corresponding computational mesh) which are defined as B-spline curves. The B-spline basis functions are used as harmonic functions along the mesh boundary, being also the geometry to be deformed. Thus, any deformation of the B-spline boundary, through the movement of the curve’s control points, can be successfully propagated to the interior of the computational domain, resulting in the concurrent and conformable modification of the B-spline boundary and the entire computational mesh. For the computation of harmonic coordinates a node-centered Finite-Volume based Laplace solver for unstructured meshes is used, enhanced with an agglomeration multigrid scheme. The proposed method is applied and assessed for the shape and mesh morphing of airfoils.

A new mesh deformation method using quaternion and displacement normal propagation for high quality and high efficiency

Mesh deformation technology is widely used in aerodynamic applications like unsteady flow, aeroelasticity, and aerodynamic shape optimization because of its low computational costs and consistent mesh connectivity. In order to raise deformed mesh quality and improve efficiency, a new mesh deformation method based on quaternion and displacement normal propagation (named QN method) is introduced in this paper. The boundary points propagate their displacements composed of translational vectors and quaternions to corresponding volume points along the normal direction under the control of the damping function, which preserves the mesh shape and guarantees the quality near boundaries, including orthogonality and normal size. It can also prevent the volume points from being interfered by other less relevant boundary points, so dealing with complex displacement fields effectively. In addition, it avoids complicated matrix and interpolation operations, thus saving lots of computational costs. For mesh with complex topology, a hybrid method combining the QN method and the radial basis function method (RBF method) is investigated to broaden application scenarios, which are applied to the mesh with normal correspondence inside the boundary layer and the mesh outside, respectively. Benefitting from effective handling for the near-wall elements by the QN method, the RBF interpolation part in the hybrid method requires minor support points to carry out valid large deformation, improving the deformation efficiency greatly compared to the individual RBF method. Five typical test cases with different deformation modes and mesh characteristics are implemented, showing better performance of the proposed method in deformed mesh quality and deformation efficiency.

Influence of Seal Structure on the Motion Characteristics and Stability of a Steam Turbine Rotor

Sealing aerodynamic characteristics are affected by the seal structure, and thus the stability of the rotor system is affected too. A 1.5-stage, three-dimensional, full-cycle model of the high-pressure cylinder of a 1000 MW steam turbine was established. The high eccentricity whirl of the rotor was realized using mesh deformation technology and the multi-frequency whirl model. The nonlinear steam-flow-exciting force of different sealing structures was obtained using CFD/FLUENT, and the motion equations with a nonlinear steam-exciting force were solved using the Runge–Kutta method. The motion characteristics and stability of the rotor system with different sealing structures were obtained. The results show that there are “inverted bifurcation” and “double bifurcation” phenomena in the bifurcation diagrams of different tooth numbers, boss numbers, and tooth lengths, and a 1/2 power frequency of different sealing structures goes through the process of weakening, disappearing, reproducing, and evolving into a 1/3 power frequency and a 2/3 power frequency. With the increasing load, the steam-flow-exciting force becomes stronger, and the multi-frequency vibration and dense frequency phenomena are significant. Under some load conditions, the change curves of three kinds of teeth in 1/3 and 2/3 power frequency vibrations are highly similar, and the tooth number has little influence on the system stability. Under the high load condition, with the boss number increasing, the chaos phenomenon is weakened. Increasing the tooth length is beneficial to the stability of the rotor.

Surface pressure reconstruction from LPT data with boundary conforming grids

Abstract The coupling between fluid-structure interactions is governed by the pressure distribution over the interaction surface between the fluid and solid domains. The capabilities of non-intrusive optical techniques, such as particle image velocimetry and Lagrangian particle tracking (LPT), have been proven to provide accurate velocity and acceleration information within the flow field while simultaneously tracking the corresponding structural deformations. However, scattered data from LPT measurements are typically mapped onto Cartesian grids, independently of the shape of the solid objects in the measurement domain. The use of Cartesian grids poses challenges for the determination of the surface pressure because the velocity gradients close to the object’s surface are not captured accurately. Therefore, an alternative surface pressure reconstruction scheme utilizing LPT data based on the arbitrary Lagrangian–Eulerian approach is proposed to mitigate the error propagation associated with the use of uniform grids. The introduced method provides an exact surface conformation utilizing boundary fitted coordinate systems and radial basis function based mesh deformations, which eliminates the need to use extrapolations to obtain surface pressure distributions. The introduced approach is assessed by means of a synthetic hill surface probing a three-dimensional analytical flow field; its practical applicability is demonstrated through an experimental characterization of turbulent boundary layer interactions with a steadily and unsteadily deforming elastic membrane.

Hierarchical Spherical Cross‐Parameterization for Deforming Characters

AbstractThe demand for immersive technology and realistic virtual environments has created a need for automated solutions to generate characters with morphological variations. However, existing approaches either rely on manual labour or oversimplify the problem by limiting it to static meshes or deformation transfers without shape morphing. In this paper, we propose a new cross‐parameterization approach that semi‐automates the generation of morphologically diverse characters with synthesized articulations and animations. The main contribution of this work is that our approach parameterizes deforming characters into a novel hierarchical multi‐sphere domain, while considering the attributes of mesh topology, deformation and animation. With such a multi‐sphere domain, our approach minimizes parametric distortion rates, enhances the bijectivity of parameterization and aligns deforming feature correspondences. The alignment process we propose allows users to focus only on major joint pairs, which is much simpler and more intuitive than the existing alignment solutions that involve a manual process of identifying feature points on mesh surfaces. Compared to recent works, our approach achieves high‐quality results in the applications of 3D morphing, texture transfer, character synthesis and deformation transfer.

Dynamic and Phase-Frequency Characteristics of Rotor Instability Induced by Steam Flow Excited Vibration in Seals

Steam flow excited vibration in seals seriously affects the seal-rotor stability. A mesh deformation based on user-defined functions was adopted to establish the multi-frequency whirl model, and the reliability of the simulation method was verified by experiments. The average effective damping and working ability of the fluid were proposed to analyse the stability of the seal. The mechanism of seal instability induced by steam flow excited vibration was revealed through the phase-frequency characteristics of exciting forces and displacements. The results show that direct damping decreases gradually with an increase in frequency, and the cross-coupling damping tends to be stable over 15 Hz. The average effective damping is more sensitive and accurate in predicting the seal stability. Effective damping decreases with increased frequency. Therefore, the rotor stability is decreased. Near the 12 Hz and 24 Hz frequencies, the average effective damping of eccentricity fluctuates, so the seal stability is poor. The negative effect of exciting forces increases, and the seal stability is improved when the eccentricity increases. When the phase difference between the excitation force and displacement changes, the seal stability decreases. The fundamental reason for rotor instability induced by steam flow excited vibration in seals is the sharp changes of phase difference caused by pressure fluctuations.

Application of deep learning reduced-order modeling for single-phase flow in faulted porous media

AbstractOur research is positioned within the framework of subsurface resource utilization for sustainable economies. We concentrate on modeling the underground single-phase fluid flow affected by geological faults using numerical simulations. The study of such flows is characterized by strong uncertainites in the data defing the problem due to the difficulty of taking precise measurements in the subsoil. We aim to demonstrate the feasibility of a reduced order model that is both reliable and computationally efficient, thereby facilitating the incorporation of uncertainties. We account for the uncertainities of the properties of the rock and the geometry of the fault. The latter is achieved by using a radial basis function mesh deformation method. This approach benefits from a mixed-dimensional framework to model the rock matrix and faults as n and $${n-1}$$ n - 1 dimensional domains, allowing for non-conforming meshes. Our primary focus is on a reduced-order model capable of reproducing flow variables across the entire domain. We utilize the Deep Learning Reduced Order Model (DL-ROM), a nonintrusive neural network-based technique, and we compare it against the traditional Proper Orthogonal Decomposition (POD) method across various scenarios. The most relevant contributions of this work are: the proof of concept of the use of neural network for reduced order models for subsoil flow, dealing with non-affine problems and mixed dimensional domain. Additionally, we generalize an existing mesh deformation method for discontinuous deformation maps. Our analysis highlights the capability of reduced order model, highlighting DL-ROM’s capacity to expedite complex analyses with promising accuracy and efficiency, making multi-query analyses with various quantities of interest affordable.

3D displacement measurement using a single-camera and mesh deformation neural network

Computer vision-based methods for civil structure’s vibration displacement measurement have emerged as useful tools in the recent years. These methods offer several benefits including non-contact measurements, cost-effectiveness, and the ability to capture full-field displacement. Yet, there remain certain challenges. Measuring vibration displacement in 3D typically requires multiple cameras, adding complexity to camera configurations. Moreover, existing methods relied heavily on physical markers or natural key points. Placing physical markers on structures is often impractical, and natural key points are difficult to detect on structures with few distinct features or during rapid movements. Contrary to previous approaches, this paper presents a novel technique that uses a monocular camera for 3D displacement measurements. This technique obviates the need for physical markers or the reliance on natural key points, representing a significant advancement. Central to the method is a deep neural network designed to predict 3D mesh deformation directly from a single image input, combined with an initial 3D cube mesh input. A synthetic 3D dataset is generated to train the neural network. In the testing phase for real structures, advanced video segmentation method is employed to remove the background in order to enhance the prediction accuracy. The practical efficacy of this methodology is validated in a laboratory through a series of experimental tests on beam structures, demonstrating reliable results and application potentials.

Interactive Mesh Sculpting with Arbitrary Topologies in Head-Mounted VR Environments

Shape modeling is a dynamic area in computer graphics with significant applications in computer-aided design, animation, architecture, and entertainment. Virtual sculpting, a key paradigm in free-form modeling, has traditionally been performed on desktop computers where users manipulate meshes with controllers and view the models on two-dimensional displays. However, the advent of Extended Reality (XR) technology has ushered in immersive interactive experiences, expanding the possibilities for virtual sculpting across various environments. A real-time virtual sculpting system implemented in a Virtual Reality (VR) setting is introduced in this paper, utilizing quasi-uniform meshes as the foundational structure. In our innovative sculpting system, we design an integrated framework encompassing a surface selection algorithm, mesh optimization technique, mesh deformation strategy, and topology fusion methodology, which are all tailored to meet the needs of the sculpting process. The universal, user-friendly sculpting tools designed to support free-form topology are offered in this system, ensuring that the meshes remain watertight, manifold, and free from self-intersections throughout the sculpting process. The models produced are versatile and suitable for use in diverse fields such as gaming, art, and education. Experimental results confirm the system’s real-time performance and universality, highlighting its user-centric design.

Optimising shapes of multiple pin fins in a microchannel using deep reinforcement learning and mesh deformation techniques

The utilisation of advanced pin fin designs in microchannels is useful for enhancing cooling efficiency. Advancements in machine learning and processing power have sparked interest in shape optimisation techniques. This research employs a novel framework that integrates Deep Artificial Neural Networks and Reinforcement Learning with a Computational Fluid Dynamics (CFD) solver to optimise multiple pin fin shapes within a microchannel. By incorporating Radial Basis Function interpolation and Proximal Policy Optimisation alongside FLUENT, acting as the CFD environment, the reinforcement learning agent adeptly explores the design space to enhance the thermohydraulic performance factor (TPF), aiming to maximise Nusselt number while minimising pressure loss. Unlike previous heat transfer optimisation studies, which typically required mesh regeneration at each step, the proposed framework could bypass the meshing step and alter the geometry directly by relying on the RBF interpolation technique to deform the mesh directly. Three distinct scenarios investigated in this study are uniform deformation of all pin fins, deformation of all pin fins arranged in two rows, and individual deformation of each pin fin. Extensive simulations, exceeding 90,000 different cases, demonstrate that although the optimisation process for the individual deformation of each pin fin requires more iterations compared to others, it surpasses them in terms of TPF improvement. Notably, significant improvements are achieved, such as a 49 % enhancement in Nusselt number and a 33 % reduction in pressure drop, culminating in an impressive 63 % increase in TPF compared to the initial geometry.

SemanticMesh: parameterized fusion of semantic components for photogrammetric meshes

ABSTRACT The façades of photogrammetric building mesh models frequently lack detailed semantics, such as windows, doors, and balconies. To address this, we introduce SemanticMesh, a methodology designed to enrich façades with detailed semantics by integrating discrete components seamlessly. The process begins by transforming a localized area of the building surface into a 2D planar mesh using geodesic vectors, based on the component poses. This is followed by the application of 2D constrained triangulation to map the component boundaries onto the parameterized plane, after which the mesh is reconverted into 3D space. To achieve a smooth and aesthetically pleasing integration, we employ a Laplacian mesh deformation technique along the 3D embedding boundary. Our experiments across three distinct datasets – featuring near-planar, non-planar, and noisy surfaces – demonstrate that SemanticMesh provides superior modeling outcomes compared to conventional approaches.

Experimental and numerical research for the failure behavior of the dieless clinching joints

In this work, the failure behavior of dieless clinching joints for AA5052-H32 aluminum alloy under cross tension and tensile shear loading conditions was investigated by experimental and simulation methods. Firstly, The VUMAT subroutine for the N–H shear-modified GTN model (Proposed by Nahshon and Hutchinson, a modified GTN model considering hole shear effects) was written. Subsequently, the conventional and shear parameters calibration was accomplished. A finite element model (FEM) for the failure process of dieless clinching joints based on the N–H shear-modified GTN microscopic damage model, elimination of large deformation mesh distortion and consideration of the material hardening history was established. In addition, the deformation, damage and destruction processes of the joint under two loading conditions were analyzed. And macro/microfracture analysis of the failed joints was performed to further reveal the failure behavior. The results show that the established FEM of the failure process agrees well with the experimental results and failure modes are consistent. The model accuracy in predicting the tensile and shear loads is 91.9% and 92.7%, respectively. This provides a reference for the process design and joint arrangement of the dieless clinching joints in practical applications.

Dynamic Mesh Deformation Algorithms for Accurate Virtual Garment Adaptation

Dynamic Mesh Deformation Algorithms for Accurate Virtual Garment Adaptation

Reconstructing Soft Robotic Touch via In‐Finger Vision

Incorporating authentic tactile interactions into virtual environments presents a notable challenge for the emerging development of soft robotic metamaterials. In this study, a vision‐based approach is introduced to learning proprioceptive interactions by simultaneously reconstructing the shape and touch of a soft robotic metamaterial (SRM) during physical engagements. The SRM design is optimized to the size of a finger with enhanced adaptability in 3D interactions while incorporating a see‐through viewing field inside, which can be visually captured by a miniature camera underneath to provide a rich set of image features for touch digitization. Employing constrained geometric optimization, the proprioceptive process with aggregated multi‐handles is modeled. This approach facilitates real‐time, precise, and realistic estimations of the finger's mesh deformation within a virtual environment. Herein, a data‐driven learning model is also proposed to estimate touch positions, achieving reliable results with impressive R2 scores of 0.9681, 0.9415, and 0.9541 along the x, y, and z axes. Furthermore, the robust performance of the proposed methods in touch‐based human–cybernetic interfaces and human–robot collaborative grasping is demonstrated. In this study, the door is opened to future applications in touch‐based digital twin interactions through vision‐based soft proprioception.

Numerical Modeling of a Low-Cobalt All-Solid-State Cell with Ceramic Electrolyte Using a Deformable Geometry

All-solid-state batteries with a lithium negative electrode and a ceramic electrolyte are key toward high energy density. To ensure a safe, fast, accurate, and cost-effective development of this technology, the experimental methodology must be supported by the numerical modeling approach. This work proposes and describes an electrochemical model of a Li7La3Zr2O12 (LLZO) and Ni-rich NMC-based lithium cell with a deformable lithium negative electrode. Simulations were computed using the finite element method at different operating conditions to demonstrate the scope of the modeling work. Discharge rate tests, deformation tracking, geometric defect investigation, and polarization decomposition are described. Theoretical validation of the mass balance, the stripping rate, the ohmic polarization, and the mesh deformation demonstrated the consistency of the volumetric deformation strategy. We demonstrated in this study a deformable modeling strategy, which was found to be useful for the electrostripping analysis of anodic geometry defects during discharge. Non-uniformity in the lithium stripping rate was found along the anodic interface with defects, and this non-uniformity was accentuated with a higher discharge rate. The cell’s discharge potential was decomposed by considering the equilibrium potential and the polarizations of the main components of the cell. This post-processing was found to be useful for the understanding of the cell’s behavior.

Domain adaptation using AdaBN and AdaIN for high-resolution IVD mesh reconstruction from clinical MRI.

Deep learning has firmly established its dominance in medical imaging applications. However, careful consideration must be exercised when transitioning a trained source model to adapt to an entirely distinct environment that deviates significantly from the training set. The majority of the efforts to mitigate this issue have predominantly focused on classification and segmentation tasks. In this work, we perform a domain adaptation of a trained source model to reconstruct high-resolution intervertebral disc meshes from low-resolution MRI. To address the outlined challenges, we use MRI2Mesh as the shape reconstruction network. It incorporates three major modules: image encoder, mesh deformation, and cross-level feature fusion. This feature fusion module is used to encapsulate local and global disc features. We evaluate two major domain adaptation techniques: adaptive batch normalization (AdaBN) and adaptive instance normalization (AdaIN) for the task of shape reconstruction. Experiments conducted on distinct datasets, including data from different populations, machines, and test sites demonstrate the effectiveness of MRI2Mesh for domain adaptation. MRI2Mesh achieved up to a 14% decrease in Hausdorff distance (HD) and a 19% decrease in the point-to-surface (P2S) metric for both AdaBN and AdaIN experiments, indicating improved performance. MRI2Mesh has demonstrated consistent superiority to the state-of-the-art Voxel2Mesh network across a diverse range of datasets, populations, and scanning protocols, highlighting its versatility. Additionally, AdaBN has emerged as a robust method compared to the AdaIN technique. Further experiments show that MRI2Mesh, when combined with AdaBN, holds immense promise for enhancing the precision of anatomical shape reconstruction in domain adaptation.

Postoperative facial prediction for mandibular defect based on surface mesh deformation

Objectives: This study aims to introduce a novel predictive model for the post-operative facial contours of patients with mandibular defect, addressing limitations in current methodologies that fail to preserve geometric features and lack interpretability.Methods: Utilizing surface mesh theory and deep learning, our model diverges from traditional point cloud approaches by employing surface triangular mesh grids. We extract latent variables using a Mesh Convolutional Restricted Boltzmann Machines (MCRBM) model to generate a three-dimensional deformation field, aiming to enhance geometric information preservation and interpretability.Results: Experimental evaluations of our model demonstrate a prediction accuracy of 91.2 %, which represents a significant improvement over traditional machine learning-based methods.Conclusions: The proposed model offers a promising new tool for pre-operative planning in oral and maxillofacial surgery. It significantly enhances the accuracy of post-operative facial contour predictions for mandibular defect reconstructions, providing substantial advancements over previous approaches.