Engineering with Computers最新文献

Many engineering challenges involve optimising multiple criteria that often represent conflicting targets, posing significant difficulties for standard methods like gradient-based algorithms. This complexity is especially important in the context of acoustic wave propagation, where noise barriers are designed to attenuate sound pressure level (SPL). Achieving optimal performance requires carefully balancing design factors such as shape and material selection with economic constraints, making the optimisation process both technically demanding and computationally intensive. This paper proposes the development of a noise prediction surrogate model for the multi-objective optimisation of acoustic barriers. This model is developed based on data set generated employing a two-dimensional singular boundary method. The optimisation process is conducted using a multi-objective Bayesian optimisation algorithm, which is applied to the problem of acoustic line source diffraction in the presence of a porous noise barrier. Two distinct barrier configurations are considered: a straight-walled barrier and a T-shaped barrier. With a view to reduce the SPL behind the noise barrier, the set of spanned parameters includes the SPL on the side of the barrier opposite to the source, barrier's height, cap length of T-shaped barrier, porosity, tortuosity, and airflow resistivity of the material, integrating both microstructural and macrostructural aspects into the optimisation. Surface impedance boundary condition is used in the model to represent the dissipation at the surface of the noise barrier. The results demonstrate that the proposed optimisation framework enables efficient exploration of trade-offs to achieve an optimal barrier design that balances acoustic performance, material cost, and shape constraints.

Magnetic Resonance Imaging (MRI) scanners employ superconducting magnets to produce a strong uniform magnetic field over the bore of the scanner as part of the imaging process. Superconductors are preferred, as they can generate the required field strengths without electrical resistance, but, to do this, the materials need to be cooled to very low temperatures, typically around 4.2 K. However, due to imperfections in the windings, cracks and small air gaps in the epoxy resin between the wires, heating can occur leading to a process known as magnet quench. During magnet quench, the magnet temperature rises quickly, and the magnet loses its superconductivity. This work presents an accurate numerical model for predicting magnet quench for axisymmetric MRI scanners by solving the coupled system of thermal, electromagnetic and circuit equations by means of a high order/hp-version finite element method where regions of high gradients are resolved with boundary layer elements. A series of numerical results are included to demonstrate the effectiveness of the approach.

A generic side mirror can be approximated to the combination of a half cylinder topped with a quarter of sphere. The flow structure in the wake of the side mirror is highly transient and the turbulence plays an important role affecting aeroacoustics through pressure fluctuation. Thus, this geometry is one of the test cases object of several numerical studies in recent years to assess the aerodynamic and aeroacoustic capabilities of the turbulence models. In this context, this study presents how the second-generation URANS closure STRUCT-(epsilon ) is able to properly predict the expected stagnation, flow separation and vortex shedding phenomena. Besides, the predictive accuracy for the noise generation mechanism is evaluated by comparing the spectra of the sound pressure level measured at several static pressure sensors with the numerical results obtained with the STRUCT-(epsilon ). The response of this turbulence model has exceeded that from other hybrid methods and is in good agreement with the results from Large-Eddy Simulations or the experiments. To conclude the paper, the applicability of STRUCT-(epsilon ) to construct a Spectral Proper Orthogonal Decomposition method that helps identifying the most energetic modes to appropriately capture the dominant flow structures is also introduced.

Soft materials play an integral part in many aspects of modern life including autonomy, sustainability, and human health, and their accurate modeling is critical to understand their unique properties and functions. Today’s finite element analysis packages come with a set of pre-programmed material models, which may exhibit restricted validity in capturing the intricate mechanical behavior of these materials. Regrettably, incorporating a modified or novel material model in a finite element analysis package requires non-trivial in-depth knowledge of tensor algebra, continuum mechanics, and computer programming, making it a complex task that is prone to human error. Here we design a universal material subroutine, which automates the integration of novel constitutive models of varying complexity in non-linear finite element packages, with no additional analytical derivations and algorithmic implementations. We demonstrate the versatility of our approach to seamlessly integrate innovative constitutive models from the material point to the structural level through a variety of soft matter case studies: a frontal impact to the brain; reconstructive surgery of the scalp; diastolic loading of arteries and the human heart; and the dynamic closing of the tricuspid valve. Our universal material subroutine empowers all users, not solely experts, to conduct reliable engineering analysis of soft matter systems. We envision that this framework will become an indispensable instrument for continued innovation and discovery within the soft matter community at large.

This study proposes a novel framework for learning the underlying physics of phenomena with moving boundaries. The proposed approach combines Ensemble SINDy and Peridynamic Differential Operator (PDDO) and imposes an inductive bias assuming the moving boundary physics evolves in its own corotational coordinate system. The robustness of the approach is demonstrated by considering various levels of noise in the measured data using the 2D Fisher–Stefan model. The confidence intervals of recovered coefficients are listed, and the uncertainties of the moving boundary positions are depicted by obtaining the solutions with the recovered coefficients. Although the main focus of this study is the Fisher–Stefan model, the proposed approach is applicable to any type of moving boundary problem with a smooth moving boundary front without an intermediate zone of two states. The code and data for this framework is available at: https://github.com/alicanbekar/MB_PDDO-SINDy.

The recent introduction of the Least-Squares Support Vector Regression (LS-SVR) algorithm for solving differential and integral equations has sparked interest. In this study, we extend the application of this algorithm to address systems of differential-algebraic equations (DAEs) in general form. Our work presents a novel approach to solving general DAEs in an operator format by establishing connections between the LS-SVR machine learning model, weighted residual methods, and Legendre orthogonal polynomials. To assess the effectiveness of our proposed method, we conduct simulations involving various DAE scenarios, such as nonlinear systems, fractional-order derivatives, integro-differential, and partial DAEs. Finally, we carry out comparisons between our proposed method and currently established state-of-the-art approaches, demonstrating its reliability and effectiveness.

Structural reliability analysis poses significant challenges in engineering practices, leading to the development of various state-of-the-art approximation methods. Active learning methods, known for their superior performance, have been extensively investigated to estimate the failure probability. This paper aims to develop an efficient and accurate adaptive Kriging-based method for structural reliability analysis by proposing a novel learning function allocation scheme and a hybrid convergence criterion. Specifically, the novel learning function allocation scheme is introduced to address the challenge of no single learning function universally outperforms others across various engineering contexts. Six learning functions, including EFF, H, REIF, LIF, FNEIF, and KO, constitute a portfolio of alternatives in the learning function allocation scheme. The hybrid convergence criterion, combining the error-based stopping criterion with a stabilization convergence criterion, is proposed to terminate the active learning process at an appropriate stage. Moreover, an importance sampling algorithm is leveraged to enable the proposed method with the capability to deal with rare failure events. The efficiency and accuracy of the proposed method are demonstrated through four numerical examples and one engineering case.

This study introduces a two-scale graph neural operator (GNO), namely, LatticeGraphNet (LGN), designed as a surrogate model for costly nonlinear finite-element simulations of three-dimensional latticed parts and structures. LGN has two networks: LGN-i, learning the reduced compressive response of lattices, and LGN-ii, learning the mapping from the reduced representation onto the tetrahedral mesh. LGN can predict deformation for arbitrary lattices, therefore the name operator. Our approach significantly reduces inference time while maintaining a reasonable accuracy for unseen simulations, establishing the use of GNOs as efficient surrogate models for evaluating mechanical responses of lattices and structures.

The Northern European Enclosure Dam (NEED) is a hypothetical project to prevent flooding in European countries following the rising ocean level due to melting arctic glaciers. This project involves the construction of two large dams between Scotland and Norway, as well as England and France. The anticipated cost of this project is 250 to 500 billion euros. In this paper, we present the simulation of the aftermath of flooding on the European coastline caused by a catastrophic break of this hypothetical dam. From our simulation results, we can observe a traveling wave after the accident, with a velocity of approximately 45 kms per hour, raising the sea level permanently inside the dammed region. This observation implies a need to construct additional dams or barriers protecting the Netherlands’ northern coastline and the Baltic Sea’s interior. Our simulations have been obtained using the following building blocks. First, a graph transformation model was applied to generate an adaptive mesh, refined towards the seabed and the seashore topography, approximating the topography of the Earth. We employ the composition graph grammar model to break the mesh’s triangular elements without generating hanging nodes. Second, the wave equation is formulated in a spherical latitude-longitude system of coordinates and solved by a high-order time integration scheme using the generalized (alpha) method. While our paper mainly focuses on the simulation of the NEED dam break, we also provide a stand-alone tool to generate an adaptive mesh of the whole Earth. We can use our software as a stand-alone package in FEniCS or other simulation software.

A machine learning (ML) model can provide a precise prediction very quickly, if it is well trained with a massive amount of reliable training data. A finite element method (FEM) is often employed to generate substantial training data. However, such a training process can be computationally burdensome especially for a geometrically complex structure. More critically, a specific size and/or configuration of a training model may confine the applicability of the trained model to the same kind only. In this study, we present a scalable ML approach with an efficient training strategy for micromechanical analysis of fiber-reinforced composite materials. Here, a scalable data-driven micromechanics model (SDMM) is proposed for predicting stresses in unidirectional composites with random fiber arrays. The training data for SDMM is defined in the unit of a fiber pair. A single dataset is composed of a stress value between a fiber pair and an image highlighting the pair with nearby fibers affecting the stress. Therefore, the training microstructures can be considerably small, but the pairwise ML model can be applied to every pair of adjacent two fibers inside a much larger microstructure. The scalability of SDMM is demonstrated by predicting the maximum principal stress values acting between every fiber pair in a super-sized representative volume element. The accuracy of the prediction results is evaluated by finite element analysis results. It is shown that a certain number of nearby fibers is required in the training datasets for accurate prediction.