Geometric Variables Research Articles

Thermal vibration analysis of bi-directionally stepped porous functionally graded plates with segment-specific material property variation supported by Kerr foundation

This paper presents a comprehensive analysis of the nonlinear dynamic response of stepped rectangular plates made of functionally graded porous material (FGPM), supported by the Kerr foundation, in a thermal environment. A novel analytical framework is developed to explore geometric and material variations. The study investigates structural variations in plate thickness with abrupt changes in uni- or bi-directional orientations, examining both single and double stepped thickness profiles. Material properties vary with thickness, featuring distinct horizontal discontinuities across plate segments. Porosity distributions, both even and uneven, are addressed using modified mixture rules. Nonlinear kinematic relationships are established using Reddy’s third-order shear deformation plate theory and von Kármán’s nonlinear geometric assumptions, with equations of motion solved via Galerkin’s technique. This improved model effectively addresses non-continuous thickness variation through integral calculus, enhancing computational efficiency. Validation is achieved by comparing outcomes with published literature and Finite Element Analysis (FEA). The study investigates the influence of material properties, elastic foundation, boundary conditions, and geometric parameters on the free vibration and nonlinear behaviors of the plates. Some of the key findings include: increasing the thickness of the stepped segment significantly heightens the fundamental frequency while reducing vibrational amplitudes; optimizing the location of the stepped segment directly impacts the plate’s fundamental frequency and vibrational amplitudes; and as the load factor increases, the difference between linear and nonlinear deflection becomes evident. Therefore, accurate FGPM stepped plate design requires incorporating nonlinear terms in the strain–displacement relationships. Suggestions for future model modifications are also discussed, contributing to advancements in structural design and analysis.

Thermal Spreading Resistance of Near Surface Localized Heating-Induced Size Effects in Semiconductors

Abstract Localized heating is encountered in various scenarios, including the operation of transistors, light-emitting diodes, and some thermal spectroscopy techniques. When localized heating occurs on a scale comparable to the mean free path of the dominant energy carriers, additional thermal resistance is observed due to ballistic effects. The main objective of this study is to find a relation between this resistance, problem geometry, and material thermal properties in situations involving localized heating. Models based on the solution of the Fourier heat diffusion equation and the gray phonon Boltzmann Transport Equation are solved simultaneously to calculate the additional thermal resistances that arise from localized heating. Subsequently, the results are analyzed to derive the desired relationship. It is noted that in the context of localized heating resistance, the effects of geometrical variables are non-linear and substantial, particularly when the Knudsen numbers for the boundary and heat source exceed certain thresholds. Specifically, when the Knudsen number for the heat source becomes comparable to 1 localized heating resistance is observed. However, when the Knudsen number based on heat source height and width surpasses 8 and 20, respectively the heat source behaves akin to a point source, no longer significantly affecting the localized heating resistance. At this juncture, the maximum resistance limit is reached.

Comparative Analysis of Machine Learning Models for Predicting the Mechanical Behavior of Bio-Based Cellular Composite Sandwich Structures.

The growing demand for sustainable materials has significantly increased interest in biocomposites, which are made from renewable raw materials and have excellent mechanical properties. The use of machine learning (ML) can improve our understanding of their mechanical behavior while saving costs and time. In this study, the mechanical behavior of innovative biocomposite sandwich structures under quasi-static out-of-plane compression was investigated using ML algorithms to analyze the effects of geometric variations on load-bearing capacities. A comprehensive dataset of experimental mechanical tests focusing on compression loading was employed, evaluating three ML models-generalized regression neural networks (GRNN), extreme learning machine (ELM), and support vector regression (SVR). Performance indicators such as R-squared (R2), mean absolute error (MAE), and root mean square error (RMSE) were used to compare the models. It was shown that the GRNN model with an RMSE of 0.0301, an MAE of 0.0177, and R2 of 0.9999 in the training dataset, and an RMSE of 0.0874, MAE of 0.0489, and R2 of 0.9993 in the testing set had a higher predictive accuracy. In contrast, the ELM model showed moderate performance, while the SVR model had the lowest accuracy with RMSE, MAE, and R2 values of 0.5769, 0.3782, and 0.9700 for training, and RMSE, MAE, and R2 values of 0.5980, 0.3976 and 0.9695 for testing, suggesting that it has limited effectiveness in predicting the mechanical behavior of the biocomposite structures. The nonlinear load-displacement behavior, including critical peaks and fluctuations, was effectively captured by the GRNN model for both the training and test datasets. The progressive improvement in model performance from SVR to ELM to GRNN was illustrated, highlighting the increasing complexity and capability of machine learning models in capturing detailed nonlinear relationships. The superior performance and generalization ability of the GRNN model were confirmed by the Taylor diagram and Williams plot, with the majority of testing samples falling within the applicability domain, indicating strong generalization to new, unseen data. The results demonstrate the potential of using advanced ML models to accurately predict the mechanical behavior of biocomposites, enabling more efficient and cost-effective development and optimization processes in the field of sustainable materials.

An ultra-wideband antenna design optimization for biomedical applications using artificial neural networks

This paper presents an innovative method based on Artificial Neural Networks (ANN) for optimizing the design of microstrip antennas using HFSS software. The method leverages a dataset generated from comprehensive full-wave electromagnetic simulations to accurately predict antenna performance. In the ANN model, coefficients of reflection as input parameter, and five output parameters representing key geometrical variables of the antenna are utilized. The study demonstrates how ANNs effectively predict these variables, thereby enhancing design efficiency and significantly reducing the computational time required for antenna optimization. By providing a robust and efficient framework, the results highlight the promising potential of ANNs in optimizing antenna designs for diverse applications, including medical and wireless communication systems. Specifically, the proposed Ultra-Wideband (UWB) antenna operates over a frequency range from 5.17 GHz to 7.02 GHz and maintains an S11 value of -20.49 dB at 5.8 GHz within the Industrial, Scientific, and Medical (ISM) bands. This research contributes valuable insights into advancing electromagnetic performance through efficient antenna design methodologies.

Robust optimization of a novel ultraviolet (UV) photoreactor for water disinfection: A neural network approach

To optimize the ultraviolet (UV) water disinfection process, it is crucial to determine the ideal geometric dimensions of a corresponding model that enhance performance while minimizing the impact of uncertain photoreactor inputs. As water treatment directly affects people's lives, it is crucial to eliminate the risks associated with the non-ideal performance of disinfection photoreactors. Input uncertainties greatly affect photoreactor performance, making it essential to develop a robust optimization algorithm in advance to mitigate these effects and minimize the physical and financial resources required for constructing the photoreactors. In the suggested algorithm, a two-objective genetic algorithm is integrated with a non-intrusive polynomial chaos expansion (PCE) technique. Additionally, the Sobol sampling method is employed to select the necessary samples for understanding the system's behavior. An artificial neural network surrogate model is trained using sufficient data points derived from computational fluid dynamics (CFD) simulations. A novel type of UV photoreactors working based on exterior reflectors is chosen to optimize the process with three uncertain input parameters, including UV lamp power, UV transmittance of water, and diffusive fraction of the reflective surface. In addition, four geometrical design variables are considered to find the optimal configuration of the photoreactor. The standard deviation (SD) and the reciprocal of log reduction value (LRV) are set as the objective functions, calculated using PCE. The optimal design provides a LRV of 3.95 with SD of 0.2. The coefficient of variation (CoV) of the model significantly declines up to 7%, indicating the decreased sensitivity of the photoreactor to the input uncertainties. Additionally, it is discovered that the robust model exhibits minimal sensitivity to changes in reflectivity in various flow rates, and its output variability aligns with the SD obtained through robust optimization.

Some characterizations on isoquants of homogeneous and quasi-sum production functions in microeconomics

Production function is an important concept in neoclassical economics and of great significance in economics and management. In the theory of geometric variations, the minimality of surfaces describes the optimal solutions of energy equations. Due to the nature of minimum energy, beautiful shape and stable structure, the theory of minimal surfaces is widely utilized in the fields of engineering, architecture, physics and molecular chemistry. In this article, we investigate homogeneous production functions and quasi-sum production functions with minimal isoquants, which solves two problems proposed by Neacşu et al. After meticulous calculations, the complete classifications of homogeneous production functions and quasi-sum production functions are reached. The research results interestingly indicate that homogeneous production functions with minimal isoquants are closely related to harmonic functions, which is a classical concept in mathematics and have widely applications.

Influence of Laminate Twist and Clamping Pressure on Bonding Performance of Low-Grade Lumber in Cross-Laminated Timber

Abstract The geometric variations of low-grade lumber raise concerns about bond strength of cross-laminated timber (CLT) produced from such lumber. This study seeks to investigate the effect of low clamping pressure and geometric variations of laminates on the bond strength of CLT. CLT panels were manufactured from low-grade grand fir (Abies grandis). Block shear tests and cyclic delamination tests were conducted on specimens randomly taken at specific points that correspond to a wide range of twist magnitude. Twist distribution in the lumber used as laminates in the CLT ranges from 0 to 160 mm. Results showed that twist magnitude and clamping pressure have significant effect on bond performance, with twist magnitude having an overriding effect on pressure.

Energy Efficient Downsizing of Ribbed Confinements for Heat Exchange Applications

Abstract Downsizing double-pipe heat exchangers is possible by deploying ribs on the two sides of the heat exchangers. The shape of these ribs, along with two key geometric variables – pitch and height, are crucial in the selection of energy-efficient rib configurations. This is because, the enhancement in heat transfer performance comes at the cost of increased pressure drop. Thus, the goal of this three-dimensional numerical investigation is to identify favourable rib shapes and explore the effect of truncation on triangular ribs, something which is missing from existing literature. Truncation is expected to greatly affect the performance of triangular ribs, either adversely or favorably. To explore this conclusively, an unbiased and exhaustive analysis is carried out by comparing the performance of confinements with modified and regular triangular ribs, keeping plain confinements as the baseline. Furthermore, the effects of two principal design variables – rib height and rib pitch are explored for each shape. Separate results are presented for the inner and outer confinements of the double-pipe heat exchangers (pipes and annuli) to allow for the extrapolation of results for a wide range of applications employing internal flows in pipes and annuli. A phenomenological model is developed to classify the thermo-hydraulic performance of each confinement and identify optimal geometrical configuration and identify best performing design(s). Once optimal rib pitch-height combinations are identified, performance at this optimal combination is evaluated at different Reynolds numbers, spanning from 10,000 to 30,000.

Effective Stiffness and Damping Analysis of Steel Damper to Lateral Cyclic Loading

Steel dampers are components used in building structures to reduce vibration and energy generated by dynamic loads such as earthquakes. Several factors affect the effectiveness of steel dampers in reducing energy, including the cross-sectional area, mass distribution, cross-sectional geometry, and material stiffness. The cross-sectional geometry or shape of the steel damper can affect how energy is absorbed and dissipated in the structural system. Cross sections with different geometric variations can have different mechanical responses to dynamic loads. This study aims to analyze which type of steel damper is effective in terms of stiffness and damping capacity against lateral cyclic loads. The steel damper cross-sectional variations used are slit steel dampers (SSDs), tapered steel dampers (TSDs), and oval steel dampers (OSDs). Cyclic testing of the dampers used displacement control with the same target deviation for all three damper types. The results showed that the stress and strain distributions of the oval steel damper were more even than those of the other two models. The variations in the energy dissipation capacities of the three cross-section variations are relatively the same. However, the slit steel damper type has the best stiffness compared to the other two types. This research is ultimately expected to influence the science of the structure of a building in preventing and anticipating earthquakes or other disasters. Doi: 10.28991/CEJ-2024-010-07-017 Full Text: PDF

Graph theoretical analysis of limestone fracture network damage patterns based on uniaxial compression test

The topological attributes of fracture networks in limestone, subject to intense hydrodynamics and intricate geological discontinuities, substantially influence the mechanical and hydraulic characteristics of the rock mass. The dynamical evolution of fracture networks under stress is crucial for unveiling the interaction patterns among fractures. However, existing models are undirected graphs focused on stationary topology, which need optimization to depict fractures' dynamic development and rupture process. To compensate for the time and destruction terms, we propose the damage network model, which defines the physical interpretation of fractures through the ternary motif. We focus primarily on the evolution of node types, topological attributes, and motifs of the fracture network in limestone under uniaxial stress. Observations expose the varying behavior of the nodes' self-dynamics and neighbors' adjacent dynamics in the fracture network. This approach elucidates the impact of micro-crack behaviors on large brittle shear fractures from a topological perspective and further subdivides the progressive failure stage into four distinct phases (isolated crack growth phase, crack splay phase, damage coalescence phase, and mechanical failure phase) based on the significance profile of the motif. Regression analysis reveals a positive linear and negative power correlation between fracture network density and branch number to the rock damage resistance, respectively. The damage network model introduces a novel methodology for depicting the interaction of two-dimensional (2D) projected fractures, considering the dynamic spatiotemporal development characteristics and fracture geometric variation. It helps dynamically characterize properties such as connectivity, permeability, and damage factors while comprehensively assessing damage in rock mass fracture networks.

Transferable machine learning model for the aerodynamic prediction of swept wings

With their development, machine learning models can be used instead of computational fluid dynamics simulations to predict flow fields in aerodynamic optimization. However, it is difficult to construct a prediction model for swept wings with various planform geometries because too many samples are required to cover the parameter space. In the present paper, a new model framework is proposed to predict wing surface pressure and friction distributions with fewer samples. The distributed geometry parameters along spanwise are used as model inputs instead of the global planform parameters, and processors are designed to help the model better learn the local effect of geometric variation. The model is trained and tested on simple swept wings with single segment and linear twist distribution, where it outperforms the global input model by 57.6% in terms of lift coefficient prediction errors on small dataset sizes. The distributed input also enables the model to be transferred from single wings to more engineering-practical yet complex kink wings. After fine-tuning with a few samples, model accuracy for kink wings can be similar to that of simple wings, which proves the model for wings with complex planform geometries can be efficiently built with the proposed method.

Transfer learning‐based physics‐informed neural networks for magnetostatic field simulation with domain variations

AbstractPhysics‐informed neural networks (PINNs) provide a new class of mesh‐free methods for solving differential equations. However, due to their long training times, PINNs are currently not as competitive as established numerical methods. A promising approach to bridge this gap is transfer learning (TL), that is, reusing the weights and biases of readily trained neural network models to accelerate model training for new learning tasks. This work applies TL to improve the performance of PINNs in the context of magnetostatic field simulation, in particular to resolve boundary value problems with geometrical variations of the computational domain. The suggested TL workflow consists of three steps. (a) A numerical solution based on the finite element method (FEM). (b) A neural network that approximates the FEM solution using standard supervised learning. (c) A PINN initialized with the weights and biases of the pre‐trained neural network and further trained using the deep Ritz method. The FEM solution and its neural network‐based approximation refer to an computational domain of fixed geometry, while the PINN is trained for a geometrical variation of the domain. The TL workflow is first applied to Poisson's equation on different 2D domains and then to a 2D quadrupole magnet model. Comparisons against randomly initialized PINNs reveal that the performance of TL is ultimately dependent on the type of geometry variation considered, leading to significantly improved convergence rates and training times for some variations, but also to no improvement or even to performance deterioration in other cases.

Efficient Numerical Solution of the Sharma-Tasso-Olver Equation Using Radial Basis Function-Pseudo Spectral Method with Comparative Analysis

In this study, we will solve the Sharma-Tasso-Olver equation by utilizing the radial basis function-pseudo spectral approach. Pseudo spectral techniques are renowned for their exceptional precision but have constraints regarding their ability to handle geometric variations. The integration of Radial Basis Functions (RBF) with pseudo spectral methods offers a solution to surmount this particular limitation. To estimate the differentials with respect to the variable , we will use the radial basis functions, including commonly used ones like multiquadric, inverse multiquadric, and inverse quadric, have been employed. Furthermore, the problem has transformed into the ordinary differential equation (ODE), and the solution to this ODE system has been acquired through the application of the fourth-order Runge-Kutta method. Additionally, a comparative analysis has been conducted between the solutions obtained through the suggested technique and the exact solutions.

Design of Heavy-Load Soft Robots Based on a Dual Biomimetic Structure.

This study first draws inspiration from the dual biomimetic design of plant cell walls and honeycomb structures, drawing on their structural characteristics to design a flexible shell structure that can achieve significant deformation and withstand large loads. Based on the staggered bonding of this flexible shell structure, we propose a new design scheme for a large-load pneumatic soft arm and establish a mathematical model for its flexibility and load capacity. The extension and bending deformation of this new type of soft arm come from the geometric variability of flexible shell structures, which can be controlled through two switches, namely, deflation and inflation, to achieve extension or bending actions. The experimental results show that under a driving pressure within the range of 150 kpa, the maximum elongation of the soft arm reaches 23.17 cm, the maximum bending angle is 94.2 degrees, and the maximum load is 2.83 N. This type of soft arm designed based on dual bionic inspiration can have both a high load capacity and flexibility. The research results provide new ideas and methods for the development of high-load soft arms, which are expected to expand from laboratories to multiple fields.

AI-enabled damage estimation of hurricane-impacted residential communities through CFD simulations and stratified sampling

This study introduces a novel framework for estimating hurricane-induced damage to individual buildings by integrating AI-based 3D building modeling, Computational Fluid Dynamics (CFD), and stratified sampling. By leveraging the precise geometric characteristics of individual buildings extracted from digital data, the framework enables the accurate computation of pressure distributions for individual buildings through high-fidelity CFD simulations. The subsequent integration of an AI-based building component detection technique with vulnerability modeling and enhanced stratified sampling enables the rapid computation of the component and building-level failure probabilities. Applied to a case study in Atlantic City, NJ, the research underscores the effectiveness of realistic geometric characterizations and the inclusion of neighboring buildings in estimating individual-building level damage. Unlike conventional archetype-based approaches, the proposed methodology offers individual-building level risk assessment, reflecting the crucial role played by geometric and aerodynamic variables. The framework offers a promising step towards automated high-fidelity assessments of hurricane risks, leveraging detailed modeling and simulation to contribute to more resilient communities.

Development of a control co-design optimization framework with aeroelastic-control coupling for floating offshore wind turbines

Sequential design optimization is frequently used to design floating offshore wind turbines. This method simplifies the design problem by implementing each of the disciplines successively and independently with the direct/dynamic coupling between them neglected. However, if the coupling between disciplines is strong, the global optimum may not be found by sequential design. Control co-design optimization is an approach that integrates all physics in a single optimization formulation to achieve the global optimum. However, it is challenging to achieve due to the highly complex system-control coupling in floating offshore wind turbines. This paper describes a control co-design optimization framework developed for floating offshore wind turbines. In particular, the blade airfoil chord and twist distributions are parameterized to change aerodynamics while the blade structural characteristics can be updated. The rotor speed and blade pitch controllers are tuned automatically based on the properties of the new plant model. For demonstration, a 10 MW reference wind turbine is used in the case study. The annual energy production is maximized, and the blade mass is minimized. The blade shape and control parameters are optimized simultaneously, subject to constraints on geometric design variables, fatigue loads, ultimate loads, and blade deflection. To solve this high-dimensional, nonlinear, and constrained problem, a genetic algorithm is used, and the Pareto front of the design space is found. The design solutions on the Pareto front showed an improvement in the blade mass and annual energy production. These design alternatives also showed reduced structural ultimate and fatigue loads, and generator torque. This revealed the potential of control co-design to reach a better performance than the sequential method.

Assessment of the Aerodynamic and Aeroelastic Performance of Long-Span Twin-Box Bridges Based upon Multidimensional Surrogate Models

Twin-box decks are being extensively used in the design of long-span bridges due to their superior flutter performance. Although the significant role played by the gap distance has been previously addressed in the frame of experimental studies, there is still a lack of understanding about the complex interplay between box geometry, gap distance and the aerodynamic force coefficients and flutter derivatives. In the present work, firstly, a surrogate model is developed, considering three design inputs for the geometry of the deck along with the angle of attack, providing the force coefficients as output. Afterwards, the work is then extended by developing another surrogate model, considering as inputs the reduced velocity and the same geometric variables of the deck, with the outputs being the flutter derivatives. The methodology, comprising the selection of the design domain, the definition of the samples, the CFD-based evaluation of the samples' response and the construction of the surrogate through the application of the neural network-based radial basis method, is reviewed. The surrogate models enable a quantitative description of the impact caused in the force coefficients and the flutter derivatives by modifications in the geometry of the twin-box deck. It has been found that flutter derivatives H1*, H2* and A2* are strongly dependent on the gap distance.

The role of process and geometrical parameters of gate stack Inverted-T shape junction less FET at 20 nm technology node

In the present era, FET devices are seamlessly integrated with complex circuits that can be used to realize chips capable of operating at a significantly higher speed. These designed devices can meet the requisite of circuits used in designing mobile phones, tablets and laptops, thereby enhancing the overall performance. The present work focuses on designing of gate stack inverted-T junctionless FET at 20 nm technology node. Both static and low frequency characteristics of device such as threshold voltage (VTh), transconductance (gm), on (ION) & off (IOFF) state currents, as well as its high-frequency characteristics like total gate capacitance (Cgg), cut-off frequency (fT), maximum oscillation frequency (fmax) and gain bandwidth product (GBW) are methodically demonstrated with alterations in the device process and geometrical variations. The parameters such as height of fin, width of fin and work function are varied to study their impact. The SS, DIBL, ION/IOFF (current switching ratio) and VTh, for 20 nm gate length are observed as 67.94 mV/dec, 34.32 mV/V, 108, 0.32 V, respectively. The fmax is determined to be in the THz range and has early voltage of approximately 6.12V. Additionally, the effect of temperature is also studied by varying it in the range of 250 K–450 K. Analysis and virtual modelling of device is carried out using Visual TCAD. In addition, p-channel transistor is designed along with the n-channel configuration to investigate the device performance for CMOS based circuits. The noise margin and average delay of the inverter circuit are observed to be 0.37 V and 36.7 ps respectively.

Numerical and experimental study on the critical geometric variation based on sensitivity analysis on a compressor rotor

Abstract Different types of geometric variations often appear coupledly in manufactured blades. It is desired to identify the ones that have the strongest impact on the performance. In this paper, the influence of multiple geometric variations on the compressor rotor blade at the design point is studied. Two hundred and nine varied blades are constructed by adding variation data to the design-intent blade and assessed using steady-state Reynolds-Averaged Navier–Stokes (RANS) simulations. It is shown that the region near the lower and upper end of the blade is more sensitive to geometric variations. Spearman’s rank correlation coefficient is used to measure the sensitivity of rotor performance to geometric variations. The results show that the stagger angle variation is the most influencing variation. Local sensitivity analysis at different spans also shows that the stagger angle variation is an important geometric variation that needs more attention during blade manufacturing. Also, the influence of geometric variations can reach further than nearby regions. Consequently, experiments on linear cascades at different spans of the rotor are carried out to study the effect of stagger angle on flow characteristics. Results show that the stagger angle variation could lead to different changes in performance depending on the specific cascade profile.

Role of the vessel morphology on the lenticulostriate arteries hemodynamics during atrial fibrillation: A CFD-based multivariate regression analysis

Background and objectiveAtrial fibrillation (AF) is the most common cardiac arrhythmia, inducing accelerated and irregular beating. Beside well-known disabling symptoms - such as palpitations, reduced exercise tolerance, and chest discomfort - there is growing evidence that an alteration of deep cerebral hemodynamics due to AF increases the risk of vascular dementia and cognitive impairment, even in the absence of clinical strokes. The alteration of deep cerebral circulation in AF represents one of the least investigated among the possible mechanisms. Lenticulostriate arteries (LSAs) are small perforating arteries mainly departing from the middle cerebral artery (MCA) and susceptible to small vessel disease, which is one of the mechanisms of subcortical vascular dementia development. The purpose of this study is to investigate the impact of different LSAs morphologies on the cerebral hemodynamics during AF. MethodsBy combining a computational fluid dynamics (CFD) analysis of LSAs with 7T high-resolution magnetic resonance imaging (MRI), we performed different CFD-based multivariate regression analyses to detect which geometrical and morphological vessel features mostly affect AF hemodynamics in terms of wall shear stress. We exploited 17 cerebral 7T-MRI derived LSA vascular geometries extracted from 10 subjects and internal carotid artery data from validated 0D cardiovascular-cerebral modeling as inflow conditions. ResultsOur results revealed that few geometrical variables - namely the size of the MCA and the bifurcation angles between MCA and LSA - are able to satisfactorily predict the AF impact. In particular, the present study indicates that LSA morphologies exhibiting markedly obtuse LSA-MCA inlet angles and small MCA size downstream of the LSA-MCA bifurcation may be more prone to vascular damage induced by AF. ConclusionsThe present MRI-based computational study has been able for the first time to: (i) investigate the net impact of LSAs vascular morphologies on cerebral hemodynamics during AF events; (ii) detect which combination of morphological features worsens the hemodynamic response in the presence of AF. Awaiting necessary clinical confirmation, our analysis suggests that the local hemodynamics of LSAs is affected by their geometrical features and some LSA morphologies undergo greater hemodynamic alterations in the presence of AF.