Engineering with Computers最新文献

A thin shell formulation is developed for the approximation by a meshfree Reproducing Kernel Particle Method (RKPM). The formulation is derived from a degenerated shell approach where the structure is treated as a 3D solid subjected to kinematic constraints of the Kirchhoff–Love (KL) shell theory. To address the challenge of surface geometry representation in a meshfree method, a local parameterization using principal component analysis (PCA) is employed. Taylor-series expansion adapted to the shell formulation is developed to address the accuracy and stability issues of nodal quadrature. Several approaches that address membrane locking are also considered. The effectiveness of the proposed RKPM KL shell formulation is demonstrated using an extensive set of linear-elastic and finite-deformation inelastic test cases.

This study proposes a shape optimisation framework for unsteady electromagnetic scattering problems on the basis of the time-domain boundary integral equation method, focusing on the perfectly electric conductors (PECs). The boundary-only formulation is ideal for treating a shape optimisation problem in an exterior domain. However, the electromagnetic shape optimisation in concern has been unrealised with the boundary integral approach regardless of the fact that the boundary-type shape derivative has been known in the literature. The first contribution of the present study is to derive a novel expression of the shape derivative in terms of the surface current densities of the primary and adjoint problems, by considering that the surface current density is handled by usual integral equations methods. The second contribution is to clarify the integral representations and equations of the adjoint electromagnetic fields in terms of the reversal time. These theoretical achievements possess a high affinity with the standard spatial discretising approach (i.e. RWG basis) whenever the temporal basis is sufficiently smooth. The numerical experiments confirmed the reliability of the proposed shape optimisation methodology and indicated the capability to deal with scientific and engineering applications.

It is vital to control the vibration of cellular composites under harmonic excitation in engineering. Due to numerous design variables and expensive frequency domain integration operation, the majority of multiscale topology optimization methods for frequency response minimization of cellular composites tend to be conservative, where a small number of types of microstructures are considered. This paper proposes an efficient multiscale topology optimization method to minimize the frequency response of cellular composites over specified frequency intervals. This method utilizes multiclass graded lattice unit cells (LUCs) as design candidates, offering great design space to improve the dynamic performance of cellular composites. At microscale, the proposed method leverages Kriging metamodels to replace the the homogenization method in each iteration step, thus accelerating the performance estimation of multiclass graded LUCs. At macroscale, the second-order Krylov subspace with moment-matching Gram-Schmidt orthonormalization (SOMMG) method is introduced to expedite the frequency response analysis of cellular composites. Two types of design variables are employed to construct the Kriging metamodel assisted Uniform Multiphase Materials Interpolation (KUMMI) model, facilitating the concurrent updating of LUCs’ classes and relative densities. Several numerical examples are presented to validate the effectiveness and efficiency of the proposed method in minimizing the frequency response of cellular composites.

Efficient and robust anisotropic mesh adaptation is crucial for Computational Fluid Dynamics (CFD) simulations. The CFD Vision 2030 Study highlights the pressing need for this technology, particularly for simulations targeting supercomputers. This work applies a fine-grained speculative approach to anisotropic mesh operations. Our implementation exhibits more than 90% parallel efficiency on a multi-core node. Additionally, we evaluate our method within an adaptive pipeline for a spectrum of publicly available test-cases that includes both analytically derived and error-based fields. For all test-cases, our results are in accordance with published results in the literature. Support for CAD-based data is introduced, and its effectiveness is demonstrated on one of NASA’s High-Lift prediction workshop cases.

Machine learning is employed for solving physical systems governed by general nonlinear partial differential equations (PDEs). However, complex multi-physics systems such as acoustic-structure coupling are often described by a series of PDEs that incorporate variable physical quantities, which are referred to as parametric systems. There are lack of strategies for solving parametric systems governed by PDEs that involve explicit and implicit quantities. In this paper, a deep learning-based Multi Physics-Informed PointNet (MPIPN) is proposed for solving parametric acoustic-structure systems. First, the MPIPN introduces an enhanced point-cloud architecture that encompasses explicit physical quantities and geometric features of computational domains. Then, the MPIPN extracts local and global features of the reconstructed point-cloud as parts of solving criteria of parametric systems, respectively. Besides, implicit physical quantities are embedded by encoding techniques as another part of solving criteria. Finally, all solving criteria that characterize parametric systems are amalgamated to form distinctive sequences as the input of the MPIPN, whose outputs are solutions of systems. The proposed framework is trained by adaptive physics-informed loss functions for corresponding computational domains. The framework is generalized to deal with new parametric conditions of systems. The effectiveness of the MPIPN is validated by applying it to solve steady parametric acoustic-structure coupling systems governed by the Helmholtz equations. An ablation experiment has been implemented to demonstrate the efficacy of physics-informed impact with a minority of supervised data. The proposed method yields reasonable precision across all computational domains under constant parametric conditions and changeable combinations of parametric conditions for acoustic-structure systems.

Many natural materials exhibit highly complex, nonlinear, anisotropic, and heterogeneous mechanical properties. Recently, it has been demonstrated that data-driven strain energy functions possess the flexibility to capture the behavior of these complex materials with high accuracy while satisfying physics-based constraints. However, most of these approaches disregard the uncertainty in the estimates and the spatial heterogeneity of these materials. In this work, we leverage recent advances in generative models to address these issues. We use as building block neural ordinary equations (NODE) that—by construction—create polyconvex strain energy functions, a key property of realistic hyperelastic material models. We combine this approach with probabilistic diffusion models to generate new samples of strain energy functions. This technique allows us to sample a vector of Gaussian white noise and translate it to NODE parameters thereby representing plausible strain energy functions. We extend our approach to spatially correlated diffusion resulting in heterogeneous material properties for arbitrary geometries. We extensively test our method with synthetic and experimental data on biological tissues and run finite element simulations with various degrees of spatial heterogeneity. We believe this approach is a major step forward including uncertainty in predictive, data-driven models of hyperelasticity.

Abstract

Imposing local boundary conditions and mitigating the surface effect at free surfaces in peridynamic (PD) models are often desired. The fictitious nodes method (FNM) “extends” the domain with a thin fictitious layer of thickness equal to the PD horizon size, and is a commonly used technique for these purposes. The FNM, however, is limited, in general, to domains with simple geometries. Here we introduce an algorithm for the mirror-based FNM that can be applied to arbitrary domain geometries. The algorithm automatically determines mirror nodes (in the given domain) of all fictitious nodes based on approximating, at each fictitious node, the “generalized” (or nonlocal) normal vector to the domain boundary. We tested the new algorithm for a peridynamic model of a classical diffusion problem with a flux singularity on the boundary. We show that other types of FNMs exhibit “pollution” of the solution far from the singularity point, while the mirror-based FNM does not and, in addition, shows a significantly faster rate of convergence to the classical solution in the limit of the horizon going to zero. The new algorithm is then used for mirror-based FNM solutions of diffusion problems in domains with curvilinear boundaries and with intersecting cracks. The proposed algorithm significantly improves the accuracy near boundaries of domains of arbitrary shapes, including those with corners, notches, and crack tips.

Graphical Abstract

In the context of surrogate-based optimization, the efficient global exploration of the design space strongly relies on the overall accuracy of the surrogate model. For most modeling approaches, significant inaccuracies are often observed at the outlier region of the design space, where very few samples are spotted, known as the “corner error”. Inspired by the Runge effect originating from equidistant samples, a Chebyshev-transformed Orthogonal Latin Hypercube sampling approach is proposed to alleviate corner errors. An initial OLH sample was generated on a unit hyper-sphere, and its radial projection was used as the start of a sequential sampling process. The acquisition function uses the confidence interval of the Kriging predictor, combined with the min–max-distance criterion. To testify the proposed approach, models built with ordinary OLH grids are compared to the models built with Chebyshev-transformed OLH grids. Benchmark tests were performed on a series of multimodal functions, four 2-dimensional functions, and three 6-dimensional functions, both the root mean-squared error and the maximum error were reduced compared with the OLH design for most of the tests. This approach was applied to increase the pressure rise of the engine cooling fan without reducing the efficiency, for which 2.5% higher pressure rise was gained compared to the reference design.

This research seeks to explore the heat shift mechanisms in a rotating system that contains a hybrid nanofluid comprising of graphene oxide and copper particles mixed with pure water, using a novel methodology. The fluid flow in a rotating system is described by mathematical equations that involve nonlinear partial differential equations (PDEs). These equations are simplified by using similarity transformations, resulting in a system of ordinary differential equations. In general, it is not feasible to find a closed-form analytical solution for nonlinear ordinary differential equations (ODEs), which implies that determining an exact mathematical expression that characterizes the behavior of the solution to such ODEs is often challenging or impossible. To that end, we have utilized the controlled learning procedure of machine learning algorithms to predict the solutions for the nonlinear nanofluid problem flowing in the rotating system. The surrogated model are developed for different cases and scenarios, to review the might of differences in various physical parameters on the profiles of the fluid. Furthermore, the solutions are supported by performing an extensive statistical analysis based on different errors. It is concluded that machine learning-based method can potentially provide insights into the underlying physics of nonlinear flow problems, which can aid in the progress of more advanced and accurate models for prognosticating the behavior of nonlinear systems.

Tau tangles in the brain cortex spread along the brain network in distinct patterns among Alzheimer's patients. We aim to simulate their network-based spreading within the cortex, tailored to each individual along the Alzheimer's continuum, without assuming any assumptions about the network architecture. A group-level intrinsic spreading network was constructed to model the pathways for the proximal and distal spreading of tau tangles by optimizing the biophysical model based on a discovery dataset of longitudinal tau positron emission tomography images for 78 amyloid-positive individuals. Group-level spreading parameters were also obtained and subsequently adjusted to produce individuated tau trajectories. By simulating these individuated tau spreading models for every individual in the discovery dataset, we successfully captured proximal and distal tau spreading, allowing reliable inferences about the underlying mechanism of tau spreading. Simulating the models also allowed highly accurate prediction of future tau topography for both discovery and independent validation datasets.