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Engineering With Computers
Journal Prestige (SJR): 0.485
Citation Impact (citeScore): 2
Number of Followers: 5  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1435-5663 - ISSN (Online) 0177-0667
Published by Springer-Verlag Homepage  [2469 journals]
  • Stiffness mapping for early detection of breast cancer: combined force and
           displacement measurements

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      Abstract: Abstract Early detection of breast cancer is crucial to patient survival. Our ultimate goal is to automate and refine the manual breast exam process using an electromechanical device that gently indents the tissue at multiple locations and measures the required indentation forces and the resulting displacements of the tissue surface. Our current experiments use a simplified electromechanical device and average-sized (180 cc) silicone breast phantoms to collect the force/displacement data. This data is used with finite element methods and a genetic algorithm to create a three-dimensional map of the stiffness inside the breast—unusually stiff regions are suspected tumors. We tested 14 tumor-free phantoms and 14 tumor-containing phantoms. Using a combination of 10% force data and 90% displacement data we could correctly classify all phantoms as tumor-containing or tumor-free, which was substantially more robust than either measurement modality alone. In addition, the approach is robust to errors in the stiffness assumed in the computations.
      PubDate: 2022-09-28
       
  • Space-time hp-finite elements for heat evolution in laser powder bed
           fusion additive manufacturing

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      Abstract: Abstract The direct numerical simulation of metal additive manufacturing processes such as laser powder bed fusion is challenging due to the vast differences in spatial and temporal scales. Classical approaches based on locally refined finite elements combined with time-stepping schemes can only address the spatial multi-scale nature and provide only limited scaling potential for massively parallel computations. We address these shortcomings in a space-time Galerkin framework where the finite element interpolation also includes the temporal dimension. In this setting, we construct four-dimensional meshes that are locally refined towards the laser spot and allow for varying temporal accuracy depending on the position in space. By splitting the mesh into conforming time-slabs, we recover a stepwise solution to solve the space-time problem locally in time at this slab; additionally, we can choose time-slab sizes significantly larger than classical time-stepping schemes. As a result, we believe this setting to be well suited for large-scale parallelization. In our work, we use a continuous Galerkin–Petrov formulation of the nonlinear heat equation with an apparent heat capacity model to account for the phase change. We validate our approach by computing the AMB2018-02 benchmark, where we obtain an excellent agreement with the measured melt pool shape. Using the same setup, we demonstrate the performance potential of our approach by hatching a square area with a laser path length of about one meter.
      PubDate: 2022-09-25
       
  • Uncertain dynamic topology optimization based on the interval reliability
           evaluation and equivalent static loads (ESLs) algorithm

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      Abstract: Abstract This study investigates an interval reliability-based topology optimization (IRBTO) scheme for the lightweight design of continuum structures under unknown-but-bounded (UBB) dynamic performances. The dynamic response equation is first discretized, and the optimal design can be further converted into a multi-case time-invariant format using the equivalent static loads (ESLs) approach. In view of the inevitability of multi-source uncertainties during the whole design optimization procedure, the set quantitative model and interval dimension-by-dimension method (IDDM) are proposed for the acquisition of the reasonable characterization of uncertain dynamic responses in each iterative layout configuration. For reasons of structural safety and robustness, a new non-probabilistic reliability index oriented to structural average dynamic compliance is defined via the set-interference principle, and its design sensitivity for each elemental intermediate density is correspondingly analyzed. The method of moving asymptotes (MMA) is employed as the optimization problem solver. The usage and validity of the proposed techniques are demonstrated with several numerical examples, eventually.
      PubDate: 2022-09-20
       
  • Modified nonlocal couple stress isogeometric approach for bending and free
           vibration analysis of functionally graded nanoplates

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      Abstract: Abstract In this article, isogeometric analysis (IGA) based on the modified nonlocal couple stress theory (MNCST) is introduced to study bending and free vibration characteristics of functionally graded (FG) nanoplates placed on an elastic foundation (EF). The MNCST is a combination of nonlocal elasticity theory and modified couple stress theory to capture the small-size effects most accurately, hence this theory considers both softening and stiffening effects on responses of FG nanoplates. A higher order refined plate theory is adapted, because it satisfies parabolic distributions of transverse shear stresses across the nanoplate thickness and equals zero at the top and bottom surfaces without requiring shear correction factors. The governing equations are obtained using Hamilton's principle from which deduce the equations determining the natural frequency and displacement of the FG nanoplates. Several comparison studies are conducted to verify the proposed model with other results in the literature. Furthermore, the influence of nonlocal parameters, material length parameters, boundary conditions, material volume exponent on the bending, and free vibration response of FG nanoplates are fully studied.
      PubDate: 2022-09-20
       
  • A multi-fidelity active learning method for global design optimization
           problems with noisy evaluations

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      Abstract: Abstract A multi-fidelity (MF) active learning method is presented for design optimization problems characterized by noisy evaluations of the performance metrics. Namely, a generalized MF surrogate model is used for design-space exploration, exploiting an arbitrary number of hierarchical fidelity levels, i.e., performance evaluations coming from different models, solvers, or discretizations, characterized by different accuracy. The method is intended to accurately predict the design performance while reducing the computational effort required by simulation-driven design (SDD) to achieve the global optimum. The overall MF prediction is evaluated as a low-fidelity trained surrogate corrected with the surrogates of the errors between consecutive fidelity levels. Surrogates are based on stochastic radial basis functions (SRBF) with least squares regression and in-the-loop optimization of hyperparameters to deal with noisy training data. The method adaptively queries new training data, selecting both the design points and the required fidelity level via an active learning approach. This is based on the lower confidence bounding method, which combines the performance prediction and the associated uncertainty to select the most promising design regions. The fidelity levels are selected considering the benefit-cost ratio associated with their use in the training. The method’s performance is assessed and discussed using four analytical tests and three SDD problems based on computational fluid dynamics simulations, namely the shape optimization of a NACA hydrofoil, the DTMB 5415 destroyer, and a roll-on/roll-off passenger ferry. Fidelity levels are provided by both adaptive grid refinement and multi-grid resolution approaches. Under the assumption of a limited budget for function evaluations, the proposed MF method shows better performance in comparison with the model trained by high-fidelity evaluations only.
      PubDate: 2022-09-20
       
  • Data-driven modeling of the mechanical behavior of anisotropic soft
           biological tissue

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      Abstract: Abstract Closed-form constitutive models are currently the standard approach for describing soft tissues’ mechanical behavior. However, there are inherent pitfalls to this approach. For example, explicit functional forms can lead to poor fits, non-uniqueness of those fits, and exaggerated sensitivity to parameters. Here we overcome some of these problems by designing deep neural networks (DNN) to replace such explicit expert models. One challenge of using DNNs in this context is the enforcement of stress-objectivity. We meet this challenge by training our DNN to predict the strain energy and its derivatives from (pseudo)-invariants. Thereby, we can also enforce polyconvexity through physics-informed constraints on the strain-energy and its derivatives in the loss function. Direct prediction of both energy and derivative functions also enables the computation of the elasticity tensor needed for a finite element implementation. Then, we showcase the DNN’s ability by learning the anisotropic mechanical behavior of porcine and murine skin from biaxial test data. Through this example, we find that a multi-fidelity scheme that combines high fidelity experimental data with a low fidelity analytical approximation yields the best performance. Finally, we conduct finite element simulations of tissue expansion using our DNN model to illustrate the potential of data-driven approaches such as ours in medical device design. Also, we expect that the open data and software stemming from this work will broaden the use of data-driven constitutive models in soft tissue mechanics.
      PubDate: 2022-09-16
       
  • Integrating material selection with design optimization via neural
           networks

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      Abstract: The engineering design process often entails optimizing the underlying geometry while simultaneously selecting a suitable material. For a certain class of simple problems, the two are separable where, for example, one can first select an optimal material, and then optimize the geometry. However, in general, the two are not separable. Furthermore, the discrete nature of material selection is not compatible with gradient-based geometry optimization, making simultaneous optimization challenging. In this paper, we propose the use of variational autoencoders (VAE) for simultaneous optimization. First, a data-driven VAE is used to project the discrete material database onto a continuous and differentiable latent space. This is then coupled with a fully-connected neural network, embedded with a finite-element solver, to simultaneously optimize the material and geometry. The neural-network’s built-in gradient optimizer and back-propagation are exploited during optimization. The proposed framework is demonstrated using trusses, where an optimal material needs to be chosen from a database, while simultaneously optimizing the cross-sectional areas of the truss members. Several numerical examples illustrate the efficacy of the proposed framework. The Python code used in these experiments is available at github.com/UW-ERSL/MaTruss. Graphical abstract
      PubDate: 2022-09-16
       
  • Estimating external tissue support parameters with fluid–structure
           interaction models from 4D ultrasound of murine thoracic aortae

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      Abstract: Modeling of fluid–structure interactions (FSIs) between the deformable arterial wall and blood flow is necessary to obtain physiologically realistic computational models of cardiovascular systems. However, lack of information on the nature of contact between the outer vessel wall and surrounding tissue presents challenges in prescribing appropriate structural boundary conditions. Imaging techniques used to visualize wall deformation in vivo may be useful for estimating simulation parameters that capture the effects of both vascular composition and surrounding tissue support on the vessel wall displacement. Here, we present a method to calibrate external tissue support parameters in FSI simulations against four-dimensional ultrasound (4DUS) of the murine thoracic aortae. We collected ultrasound, blood pressure, and histological data from several mice infused with angiotensin II ( \(n=4\) ) and created a representative model of healthy and diseased (at 28 days post-angiotensin II infusion) murine aortae. We ran pulsatile FSI simulations after accounting for increased arterial wall stiffness with varying levels of tissue support, which demonstrated non-trivial variation in not only structural quantities, such as vessel wall deformation, but also hemodynamic quantities, such as wall shear stress across simulations. Furthermore, we compared simulation results with in vivo 4DUS imaging data and observed that the suitable range of the tissue support spring parameter was identical for both healthy and diseased states. This indicated that the same tissue support parameter estimates could be used for modeling the healthy and diseased states of the vessel, provided that changes in arterial wall stiffness had been considered. We anticipate this technique and the tissue support estimates reported herein will help inform computational models of blood flow and vasculature that incorporate the influence of external tissue. Graphical abstract
      PubDate: 2022-09-14
       
  • A versatile SPH modeling framework for coupled microfluid-powder dynamics
           in additive manufacturing: binder jetting, material jetting, directed
           energy deposition and powder bed fusion

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      Abstract: Abstract Many additive manufacturing (AM) technologies rely on powder feedstock, which is fused to form the final part either by melting or by chemical binding with subsequent sintering. In both cases, process stability and resulting part quality depend on dynamic interactions between powder particles and a fluid phase, i.e., molten metal or liquid binder. The present work proposes a versatile computational modeling framework for simulating such coupled microfluid-powder dynamics problems involving thermo-capillary flow and reversible phase transitions. In particular, a liquid and a gas phase are interacting with a solid phase that consists of a substrate and mobile powder particles while simultaneously considering temperature-dependent surface tension and wetting effects. In case of laser–metal interactions, the effect of rapid evaporation is incorporated through additional mechanical and thermal interface fluxes. All phase domains are spatially discretized using smoothed particle hydrodynamics. The method’s Lagrangian nature is beneficial in the context of dynamically changing interface topologies due to phase transitions and coupled microfluid-powder dynamics. Special care is taken in the formulation of phase transitions, which is crucial for the robustness of the computational scheme. While the underlying model equations are of a very general nature, the proposed framework is especially suitable for the mesoscale modeling of various AM processes. To this end, the generality and robustness of the computational modeling framework is demonstrated by several application-motivated examples representing the specific AM processes binder jetting, material jetting, directed energy deposition, and powder bed fusion. Among others, it is shown how the dynamic impact of droplets in binder jetting or the evaporation-induced recoil pressure in powder bed fusion leads to powder motion, distortion of the powder packing structure, and powder particle ejection.
      PubDate: 2022-09-13
       
  • Accurate computation of partial volumes in 3D peridynamics

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      Abstract: Abstract The peridynamic theory is a nonlocal formulation of continuum mechanics based on integro-differential equations, devised to deal with fracture in solid bodies. In particular, the forces acting on each material point are evaluated as the integral of the nonlocal interactions with all the neighboring points within a spherical region, called “neighborhood”. Peridynamic bodies are commonly discretized by means of a meshfree method into a uniform grid of cubic cells. The numerical integration of the nonlocal interactions over the neighborhood strongly affects the accuracy and the convergence behavior of the results. However, near the boundary of the neighborhood, some cells are only partially within the sphere. Therefore, the quadrature weights related to those cells are computed as the fraction of cell volume which actually lies inside the neighborhood. This leads to the complex computation of the volume of several cube–sphere intersections for different positions of the cells. We developed an innovative algorithm able to accurately compute the quadrature weights in 3D peridynamics for any value of the grid spacing (when considering fixed the radius of the neighborhood). Several examples have been presented to show the capabilities of the proposed algorithm. With respect to the most common algorithm to date, the new algorithm provides an evident improvement in the accuracy of the results and a smoother convergence behavior as the grid spacing decreases.
      PubDate: 2022-09-06
       
  • Correction to: A dynamic soft sensor based on hybrid neural networks to
           improve early off-spec detection

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      PubDate: 2022-08-30
       
  • Highly efficient and fully decoupled BDF time-marching schemes with
           unconditional energy stabilities for the binary phase-field crystal models
           

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      Abstract: Abstract In this paper, we present totally decoupled, efficiently linear, and energy stable schemes for solving the binary phase-field crystal model. We introduce a new auxiliary variable to reformulate the model. Based on the backward Euler formula and the second-order backward difference formula (BDF2), we construct the first- and second-order time-accurate schemes, respectively. The modified energy not only can be calculated directly from the schemes but also satisfies the energy dissipation law. In each time step, we solve two linear elliptic equations with constant coefficients and other variables are explicitly computed. The fast Fourier transform (FFT) is adopted to accelerate the convergence. Thus, the computation is highly efficient. Various benchmark numerical experiments in 2D and 3D, such as the binary crystal growth, phase separation with vacancies, are performed to show the efficiency and performance of the proposed schemes.
      PubDate: 2022-08-30
       
  • Software tools to enable immersive simulation

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      Abstract: Abstract There are two main avenues to design space exploration. In the first approach, a simulation is run, analyzed, the problem modified, and the simulation run again. In the second approach, an ensemble simulation is performed and the battery of results is leveraged to construct a surrogate model for a given quantity of interest (QoI). The first approach allows a practitioner to methodically move through the design space and analyze a solution field. A disadvantage of this technique is that each new simulation requires time-consuming setup. The second approach provides the practitioner with a global view of the problem, but requires a priori design space limits and the QoI specification. In this work, we introduce an immersive simulation software framework that enables practitioners to maintain the flexibility of the first approach, while eliminating the burden of setting up new simulations. Immersive simulation can also be used to inform the second approach, establishing limits and clarifying QoI selection prior to the launch of an ensemble simulation. We demonstrate live, reconfigurable visualization of on-going simulations coupled with live, reconfigurable problem definition that guides users in determining problem parameters. Ultimately, an immersive simulation framework enables more efficient design space exploration that reduces the gap between simulations, data analysis, and insight extraction.
      PubDate: 2022-08-26
       
  • A CNN-based surrogate model of isogeometric analysis in nonlocal
           flexoelectric problems

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      Abstract: Abstract We proposed a convolutional neural network (CNN)-based surrogate model to predict the nonlocal response for flexoelectric structures with complex topologies. The input, i.e. the binary images, for the CNN is obtained by converting geometries into pixels, while the output comes from simulations of an isogeometric (IGA) flexoelectric model, which in turn exploits the higher-order continuity of the underlying non-uniform rational B-splines (NURBS) basis functions to fast computing of flexoelectric parameters, e.g., electric gradient, mechanical displacement, strain, and strain gradient. To generate the dataset of porous flexoelectric cantilevers, we developed a NURBS trimming technique based on the IGA model. As for CNN construction, the key factors were optimized based on the IGA dataset, including activation functions, dropout layers, and optimizers. Then the cross-validation was conducted to test the CNN’s generalization ability. Last but not least, the potential of the CNN performance has been explored under different model output sizes and the corresponding possible optimal model layout is proposed. The results can be instructive for studies on deep learning of other nonlocal mech-physical simulations.
      PubDate: 2022-08-25
       
  • Sharp phase-field modeling of isotropic solidification with a super
           efficient spatial resolution

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      Abstract: Abstract The phase-field method provides a powerful framework for microstructure evolution modeling in complex systems, as often required within the framework of integrated computational materials engineering. However, spurious grid friction, pinning and grid anisotropy seriously limit the resolution efficiency and accuracy of these models. The energetic resolution limit is determined by the maximum dimensionless driving force at which reasonable model operation is still ensured. This limit turns out to be on the order of 1 for conventional phase-field models. In 1D, grid friction and pinning can be eliminated by a global restoration of Translational Invariance (TI) in the discretized phase-field equation. This is called the sharp phase-field method, which allows to choose substantially coarser numerical resolutions of the diffuse interface without the appearance of pinning. In 3D, global TI restricts the beneficial properties to a few specific interface orientations. We propose an accurate scheme to restore TI locally in the local interface normal direction. The new sharp phase-field model overcomes grid friction and pinning in three-dimensional simulations, and can accurately operate at dimensionless driving forces up to the order of \(10^{4}\) . At one-grid-point interface resolutions, exceptional degrees of isotropy can be achieved, if further the largely inhomogeneous latent heat release at the advancing solid-liquid interface is mitigated. Imposing a newly proposed source term regularization, the new model captures the formation of isotropic seaweed structures without spurious dendritic selection by grid anisotropy, even at one-grid-point interface resolutions.
      PubDate: 2022-08-24
       
  • Accurate quantification of blood flow wall shear stress using
           simulation-based imaging: a synthetic, comparative study

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      Abstract: Abstract Simulation-based imaging (SBI) is a blood flow imaging technique that optimally fits a computational fluid dynamics (CFD) simulation to low-resolution, noisy magnetic resonance (MR) flow data to produce a high-resolution velocity field. In this work, we study the effectivity of SBI in predicting wall shear stress (WSS) relative to standard magnetic resonance imaging (MRI) postprocessing techniques using two synthetic numerical experiments: steady flow through an idealized, two-dimensional stenotic vessel and a model of an adult aorta. In particular, we study the sensitivity of these two approaches with respect to the Reynolds number of the underlying flow, the resolution of the MRI data, and the noise in the MRI data. We found that the SBI WSS reconstruction: (1) is insensitive to Reynolds number over the range considered ( \(\mathrm {Re} \le 1000\) ), (2) improves as the amount of MRI data increases and provides accurate reconstructions with as little as three MRI voxels per diameter, and (3) degrades linearly as the noise in the data increases with a slope determined by the resolution of the MRI data. We also consider the sensitivity of SBI to the resolution of the CFD mesh and found there is flexibility in the mesh used for SBI, although the WSS reconstruction becomes more sensitive to other parameters, particularly the resolution of the MRI data, for coarser meshes. This indicates a fundamental trade-off between scan time (i.e., MRI data quality and resolution) and reconstruction time using SBI, which is inherently different than the trade-off between scan time and reconstruction quality observed in standard MRI postprocessing techniques.
      PubDate: 2022-08-10
       
  • An enrichment technique for bending analysis of in-plane heterogeneous
           thin plates with weak singularities

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      Abstract: Abstract Static solution of thin elastic plate problems with in-plane varying thickness or material properties having weak point singularities (e.g. crack tip or notches) is studied using a novel enrichment technique. Since the smooth basis functions are not capable of adapting to the adjacency of the singular edge point, enrichment bases called Equilibrated Singular Basis Functions (EqSBFs) are added to improve the solution quality. A combination of Chebyshev polynomials of the first kind and trigonometric functions are used as basis functions. The equilibrium equation is enforced by a weighted residual approach over a fictitious domain which contains the main problem domain. The total integration process is replaced by a composition of normalized pre-evaluated integrals, thus speeding up the procedure considerably. The novelty of the paper is that the proposed method can automatically identify and reproduce the enriching terms corresponding to the singularity order of the problem, which is an advantage with respect to the similar methods that need a priori knowledge of the analytical singularity order. Although the proposed technique is developed in the context of boundary methods, it may also be useful in other enriched methods such as XFEM.
      PubDate: 2022-08-08
       
  • A new well-balanced spectral volume method for solving shallow water
           equations over variable bed topography with wetting and drying

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      Abstract: Abstract The shallow-water equations are a hyperbolic conservation law system with source terms, which can be used in various engineering applications. Designing a high-order numerical method to preserve exactly steady-state solutions is a challenging task. Another difficulty is the appearance of dry regions in the computational domain, where no water or very little water is present. Special attention needs to be paid; otherwise, numerical methods may fail in these regions creating unphysical negative water depths. In this paper, a new high-order well-balanced Chebyshev spectral volume with a new hydrostatic reconstruction (HR) scheme is presented to preserve the steady-state solutions, and at the same time, deal with wetting and drying without loss of mass conservation. In addition, the shallow water equations may have some discontinuous solutions, even for smooth initial conditions. We modify the C-WENO limiter to reconstruct the numerical approximation on target cells that have numerical oscillations. One of the significant advantages of the modified C-WENO limiter compared to other limiters is that it only depends on the numerical approximation of the target cell and immediate neighbors. With the modified C-WENO limiter, we can achieve a high order of accuracy and non-oscillatory properties and maintain the proposed method’s well-balanced and positivity-preserving properties. To restrict the time step to the Courant–Friedrichs–Lewy condition and ensure stability and accurate results, we introduce a semi-implicit discretization of the friction source term, which does not need an iteration method. Various numerical tests are presented to evaluate the proposed method’s performance in terms of high-order accuracy, well-balanced, positivity-preserving, non-oscillatory, and mass conservation properties.
      PubDate: 2022-08-06
       
  • An efficient hierarchical fuzzy simulation method for estimating failure
           possibility

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      Abstract: Abstract The failure possibility (FP) can reasonably measure the safety degree of the structure under fuzzy uncertainty, and the estimation of FP can be transformed into searching the point with the maximum joint membership function (MF) of fuzzy input vector in the failure domain (also known as fuzzy design point). In the current fuzzy simulation (FS) method, the fuzzy design point is searched in the maximum value region of the fuzzy input vector corresponding to the lowest membership level which is equal to 0 and the computational efficiency is low. In this paper, an efficient hierarchical fuzzy simulation (HFS) method is proposed for estimating FP. In the proposed method, by the nature that the fuzzy design point is a failure point with the maximum joint MF, the fuzzy design point is first searched in the smaller value region of input vector corresponding to the larger membership level, and the membership level is automatically reduced layer by layer to expand the search region until the failure points appear. Compared with the traditional FS method, the proposed HFS method not only guarantees the search accuracy of the fuzzy design point, but also reduces the total search region; thus the computational efficiency is improved. In addition, an adaptive Kriging model is also embedded in the search process of HFS. Since the adaptively updated Kriging model is used to replace the real performance function for recognizing the state of the simulated sample points during the search process, the strategy of combining the Kriging model with HFS method can further improve the search efficiency of the fuzzy design point. The results of examples show that the proposed HFS method is reasonable and efficient.
      PubDate: 2022-08-03
       
  • An isogeometric analysis-based topology optimization framework for 2D
           cross-flow heat exchangers with manufacturability constraints

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      Abstract: Abstract Heat exchangers (HXs) have gained increasing attention due to the intensive demand of performance improving and energy saving for various equipment and machines. As a natural application, topology optimization has been involved in the structural design of HXs aiming at improving heat exchange performance (HXP) and meanwhile controlling pressure drop (PD). In this paper, a novel multiphysics-based topology optimization framework is developed to maximize the HXP for 2D cross-flow HXs, and concurrently limit the PD between the fluid inlet and outlet. In particular, an isogeometric analysis solver is developed to solve the coupled steady-state Navier–Stokes and heat convection–diffusion equations. Non-body-fitted control mesh is adopted instead of dynamically remeshing the design domain during the evolution of the boundary interface. The method of moving morphable voids is employed to represent and track boundary interface between the hot and the remaining regions. In addition, various constraints are incorporated to guarantee manufacturability of the optimized structures with respect to practical considerations in additive manufacturing, such as removing sharp corners, controlling channel perimeters, and minimizing overhangs. To implement the iterative optimization process, the method of moving asymptotes is employed. Numerical examples show that the HXP of the optimized structure is greatly improved compared with its corresponding initial design, and the PD between the fluid inlet and outlet is controlled concurrently. Moreover, a smooth boundary interface between the channel and the cold fluid, and improved manufacturability are simultaneously obtained for the optimized structures.
      PubDate: 2022-08-03
       
 
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