Layer Model Research Articles

Human versus Artificial Intelligence: Validation of a Deep Learning Model for Retinal Layer and Fluid Segmentation in Optical Coherence Tomography Images from Patients with Age-Related Macular Degeneration.

Artificial intelligence (AI) models have received considerable attention in recent years for their ability to identify optical coherence tomography (OCT) biomarkers with clinical diagnostic potential and predict disease progression. This study aims to externally validate a deep learning (DL) algorithm by comparing its segmentation of retinal layers and fluid with a gold-standard method for manually adjusting the automatic segmentation of the Heidelberg Spectralis HRA + OCT software Version 6.16.8.0. A total of sixty OCT images of healthy subjects and patients with intermediate and exudative age-related macular degeneration (AMD) were included. A quantitative analysis of the retinal thickness and fluid area was performed, and the discrepancy between these methods was investigated. The results showed a moderate-to-strong correlation between the metrics extracted by both software types, in all the groups, and an overall near-perfect area overlap was observed, except for in the inner segment ellipsoid (ISE) layer. The DL system detected a significant difference in the outer retinal thickness across disease stages and accurately identified fluid in exudative cases. In more diseased eyes, there was significantly more disagreement between these methods. This DL system appears to be a reliable method for accessing important OCT biomarkers in AMD. However, further accuracy testing should be conducted to confirm its validity in real-world settings to ultimately aid ophthalmologists in OCT imaging management and guide timely treatment approaches.

Effect of bondline thickness on the mechanical performance of CFRP laminate with asymmetric damage repaired by double-sided adhesive patch

As a common CFRP repair method, double-sided adhesive patch has been widely used in the automotive and aeronautic industry. More specific research is thus needed to help better understanding the effect of repairing parameters on the mechanical performance of CFRP structures after patch bonding. In this paper, experimental and Finite Element Modelling (FEM) efforts were made on the adhesive repaired CFRP laminate with different bondline thicknesses. Artificial damage was prefabricated in the centre of the CFRP laminate, which was then repaired through double-sided adhesive patch. Quasi-static tensile tests were conducted on the repaired specimen up to failure, to obtain the load-displacement curves. With a bondline thickness of 0.5 mm, the repaired CFRP structure reached its maximum peak load, increased by 25.3% and 26.4% compared to 0.2 mm and 1.0 mm bondline thicknesses, respectively. SEM observations were used to analyse the influence of adhesive thickness on the fracture modes. Combined with Cohesive Zone Model (CZM) for the adhesive layer and Hashin damage criterion for the CFRP, the FE model of the CFRP laminate repaired with double-sided adhesive patch subjected to tensile loading was established. Finally, the damage evolution as well as the failure modes in the parent laminate and adhesive layer with different bondline thicknesses were compared and analysed. It was revealed that bondline thickness can effectively affect the mechanical performance of CFRP laminate after adhesive patch repair.

CONVCAT: A NEW CLASSIFICATION APPROACH USING UC MERCED AND RESISC45 DATASETS

With advances in Earth observation systems, the importance of remote sensing data is increasing daily. These data are used in various fields ranging from image segmentation to terrain classification, from disaster impact assessment to climate change analysis. The use of remotely sensed images for terrain classification has been the subject of a number of studies. This study proposes a new method for terrain classification in the UC Merced Land Use Dataset and RESISC45 remote sensing images. This method is called ConvCat model, which is a combination of classical convolutional layer and CatBoost models. The performance of this model is measured in terms of accuracy, the Matthews Correlation Coefficient (MCC) and the Cohen's Kappa metrics. The results are compared with ensemble models (XGBoost, CatBoost), with ConvXGB, a combination of convolutional learning and XGBoost, and with ResNet50, one of the most widely used transfer learning models. The developed ConvCat model outperformed the other models, achieving an accuracy of 97.44% on the UC Merced data set and an accuracy of 96.89% on the Resisc45 data set. This study shows that our newly developed model provides the best results for the classification problem based on remote sensing images.

Downscaling the ocean response to the Madden–Julian Oscillation in the Northwest Atlantic and adjacent shelf seas

Subseasonal-to-seasonal (S2S) prediction is a global effort to forecast the state of the atmosphere and ocean with lead times between two weeks and a season. This study explores the feasibility of S2S prediction of the ocean using a variety of tools including statistical analysis, a statistical-dynamical mixed layer model, and a regional, high-resolution ocean circulation model based on physical principles. Ocean predictability on S2S timescales is analyzed by compositing winter sea surface temperature (SST) anomalies in the North Atlantic with respect to the state of the Madden–Julian Oscillation (MJO). It is found that statistically significant, large-scale SST changes, particularly along the eastern seaboard of North America, can be related to the MJO. This signal is shown to be driven by anomalous air–sea heat fluxes caused by atmospheric perturbations in response to the MJO. The high-resolution model of the Gulf of Maine and Scotian Shelf is used to downscale the mean ocean response to the MJO. The model is able to capture the observed relationship between the MJO and SST in the northwest Atlantic. It is also shown that the anomalous atmospheric circulation in response to the MJO leads to anomalous upwelling on the Scotian Shelf. Overall, this study demonstrates that it is feasible, and of value, to use regional ocean models for S2S prediction.

Study on the Impact of Design Factors of Piloti Forms on the Thermal Environment in Residential Quarters

Study on the Impact of Design Factors of Piloti Forms on the Thermal Environment in Residential Quarters

Fracture of four semi-regular lattices regulated by T-stress in modified boundary layer models

The development of additive manufacturing technology has promoted the rise of various lattice metamaterials, and evaluating their fracture properties is critical for both application and safety. Here, by conducting finite element simulations on four types of elastic-brittle semi-regular lattices (kagome, snub-trihexagonal, elongated-triangular, and snub-square), we indicate that the fracture toughness of lattice metamaterials is not solely determined by the crack-tip singular term, i.e., the critical stress intensity factor, but is also influenced by the non-singular T-stress term. T-stress regulates fracture toughness by modulating geometrical distortions and force-carrying modes of lattice bars around the crack front. This regulation effect depends on the lattice topology and becomes weaker under mixed-mode loading. As the relative density of lattices decreases, T-stress can force elastic buckling of bars around the crack front, which may deteriorate the fracture toughness. Additionally, positive T-stress tends to promote the crack deflection and negative T-stress tends to inhibit. Our results offer new insights into the structure-property relationships in the presence of T-stress, which may contribute to design the geometry of micro/nano-lattice metamaterials.

Temperature-driven coupled transport of pollutants and suspended particles established by granular thermodynamics

The temperature-driven effects on pollutant transport are significant, including thermal diffusion of pollutants, thermal osmosis of solutions, thermal motion of suspended particles, and other deformation and transformation processes. The pollutant seepage field equation was derived based on granular thermodynamics, and the motion equation of suspended particles was derived from Newton's second law. A comprehensive coupled transport model for the two-phase flow of pollutants and suspended particles in saturated porous media was established, which can effectively consider the impact of temperature driving. The model accurately described the coupled transport process of pollutants and suspended particles under different temperatures and particle sizes during single-pulse injection. Furthermore, the application of this model in natural clay layers under varying temperature gradients was discussed. The results indicated that, for the same time, an increase in temperature at the high-temperature end led to a 36.5 % decrease in the peak concentration of pollutant transport. When the temperature difference was significant, thermal osmosis and thermal motion were the main mechanisms for the coupled transport of pollutants and suspended particles, with the diffusion mechanism induced by concentration and temperature gradients not dominating. Thermal diffusion positively drove the transport of pollutants, while thermal osmosis and thermal motion under temperature gradients had a reverse inhibitory effect on pollutant transport.

Turbulent drag reduction using dolphin-inspired near-wall ultrasonic microvibrations

The skin-friction drag generated by wall-bounded turbulent flows can potentially be reduced by a wall-parallel oscillatory motion. Inspired by microvibrations and the high sensitivity of dolphin skin, we examine whether wall-normal undulating motion actuated by longitudinal micro-ultrasonic waves (LMUWs) with ultrasonic-frequency oscillations and micro-size amplitudes significantly alters the multi-eddy motion on the surface, thereby reducing skin-friction drag. Simulations of the LMUW-induced turbulent flows are performed in an open channel at a Reynolds number of 1.24 × 106 for three motion modes, i.e., two traveling waves (downstream and upstream) in the streamwise direction and a standing wave. It is verified that the wall-normal turbulent fluctuations are remarkedly altered within the viscous sublayer of the turbulent boundary layer, resulting in a reduced velocity gradient. This leads to lower or even extinguished friction drag, which is strongly associated with the LMUW-excitation mode. Informed and validated by numerical results, we further derived a theoretical model for the dynamic boundary layer. This model is based on Fourier series expressions of the velocities and is used to elucidate the underlying mechanisms in association with the LMUW-excited turbulent flow and active friction drag reduction. The results indicate that upstream traveling waves enable 100% friction drag reduction, while downstream traveling waves are capable of overcoming the trade-off between friction and pressure drag, accomplishing 100% total drag reduction. This study thus provides a novel active and controllable method for turbulent drag reduction.

Forecasting High Wind Events in the HRRR Model over Wyoming and Colorado. Part II: Sensitivity of Surface Wind Speeds to Model Resolution and Physics

Abstract Strong wind events can cause severe economic loss and societal impacts. Peak winds over Wyoming and Colorado occur during the wintertime months, and in the first part of this two-part series, it was shown that the High-Resolution Rapid Refresh (HRRR) model displays large negative biases with respect to strong wind events. In this part of the study, we address two questions: 1) does increasing the horizontal resolution improve the representation of strong wind events over this region, and 2) are the biases in HRRR-forecasted winds related to the selected planetary boundary layer (PBL), surface layer (SL), and/or land surface model (LSM) parameterizations? We conduct Weather Research and Forecasting (WRF) Model simulations to address these two main questions. Increasing the horizontal resolution leads to a small improvement in the simulation of strong wind speeds over the complex terrain of Wyoming and Colorado. In general, changes in the PBL, SL, and LSM parameterizations show much larger changes in simulated wind speeds compared to increasing the model resolution alone. Specifically, changing from the Mellor–Yamada–Nakanishi–Niino scheme to the Mellor–Yamada–Janjić PBL and SL schemes results in nearly no change in the r2 values, but there is a decrease in the magnitude of the strong wind speed bias (from −12.52 to −10.16 m s−1). We attribute these differences to differences in the diagnosis of surface wind speeds and mixing in the boundary layer. Further analysis is conducted to determine the value of 1-km forecasts of strong winds compared with wind speed diagnostics commonly used by the National Weather Service. Significance Statement Motivated by prior studies showing low skill in the prediction of strong winds using state-of-the-art weather forecast models, in this study, we aim to investigate two questions: 1) does increasing the horizontal resolution improve the prediction of strong wind events over the complex terrain of Wyoming and Colorado, and 2) are the biases in High-Resolution Rapid Refresh (HRRR) forecasted winds related to the selected planetary boundary layer, surface layer, and/or land surface model parameterizations? We find that increasing the horizontal resolution provides a slight improvement in the prediction of strong winds. Further, considerable improvement in the prediction of strong winds is found for varying boundary layer, surface layer, and land surface parameterizations.

A Major Improvement of Atmospheric Wave Boundary Layer Model for Storm Surge Modeling by Including Effect of Wave Breaking on Air–Sea Momentum Exchange

Abstract Accurate estimation of the wind stress under extreme conditions is crucial for modeling storm surges and storm waves, which is important to the development of a warning system for coastal disaster prevention. The problem, however, is highly challenging owing to the presence of complex ocean surface processes under the action of unusually strong wind. In this study, the existing atmospheric wave boundary layer model is significantly enhanced by including various effects of wave breaking. Both the effect of wave breaking on the dissipation of energy and its effect on the transfer of momentum within the atmospheric boundary layer are carefully formulated. The wind stress coefficients obtained with the enhanced model are shown to be in good agreement with the measurements in not only deep but also shallow waters. The enhanced atmospheric wave boundary layer model is coupled with ocean wave as well as circulation models to simulate typhoon-induced storm surges and storm waves in the Pearl River delta region. The computational results show that the coupled model with improved evaluation of the wind stress is substantially advantageous when compared with existing approaches.

Study on the Evolution Pattern of Complex Topographic Flow Field in Typical Tableland and Hogback Ridge

The flow field in complex mountainous wind farms is highly intricate, affecting the accuracy of wind resource assessments and the reliability of wind turbine operation. In order to study the evolving characteristics of the flow field in typical complex terrains under different inflow wind directions and wind speeds, this paper, based on the open-source software OpenFOAM, adopts a neutral atmospheric boundary layer model combined with Reynolds-averaged turbulence model to construct a high-precision flow field model for complex terrains. The research reveals that this model requires fewer computational resources and can effectively simulate the flow characteristics around complex terrains. Wind resource distribution in tableland terrains and hogback ridge terrains is more influenced by the height of the mountains, and the recovery speed of the wake behind the mountains decreases with a reduction in slope. In hogback ridge terrains, under a wind direction of 0 °, the wind acceleration effect at the top of the hogback ridge is stronger than under a wind direction of 270°, as it experiences more intense compression from the mountains. This paper can provide reference for micro-siting in complex terrain.

Concrete Armour Unit Breakwater Physical Model Monitoring With 3D Modeling Tools

SEABIM® is a patented scan to BIM process that can generate a reliable and complete 3D model of a rubble-mound breakwater precast protective layer (Xbloc®, Accropode™, Core-loc™ etc.). Using computer vision techniques, the known 3D shape of the Concrete Armour Units (CAU) is detected in a high-resolution point cloud, which allows to obtain the position and orientation of each unit relative to the cloud. By superimposing 3D models produced from different scans at different dates, the movement of each block can then be quantified and represented as a vector. For natural riprap armours, a point cloud segmentation algorithm is applied. Each rock is identified, and then geometric characteristics of the armour can be computed (apparent rock diameters, placement density), and the movement between each scan can be monitored. This tool has been applied to numerous real-scale projects since 2019, both in the monitoring of newly built infrastructures and in the asset management phase (Aberdeen South Harbour, Calais port 2015, Nouvelle Route du Littoral etc...). Breakwater physical models in laboratories, in wave flumes or basins, also require monitoring of the movement of reduced-scale blocks between each wave series. With SEABIM® this movement can be computed with accuracy for each CAU, allowing for a consistent evaluation of the armour layer response to wave loads. The automatic block detection process can be launched on the point cloud from a survey of the initial physical model, obtained either by photogrammetry or LiDAR. Then a new survey is done after each wave series in order to create a sequence of 3D models. Those models can be compared by computing the displacement vectors of the centers of gravity of the units. The vectors can be visualized using color graded arrows within the 3D model. All the information can then be exported to a spreadsheet for further analysis. Using this technique, settlement, rocking and sliding effects can be identified. In this paper we will discuss in detail the adaptation of the survey techniques from real scale to laboratory projects (difference in scanning technique, scale effect on point cloud density etc.). Then we will explain the 3D modeling process and the applications of the 3D models for engineering analysis purposes.

Phase composition and numerical model of low-pressure pulse carburized layer in 15Cr14Co12Mo5Ni2W steel

Aerospace engine gear steel, 15Cr14Co12Mo5Ni2W steel, requires carburizing heat treatment to achieve operational performance. However, predicting the carbon concentration during the vacuum carburizing process poses significant challenges. This paper aims to design and establish a predictive model for the carbon concentration during the vacuum carburizing process of 15Cr14Co12Mo5Ni2W steel. Given the complex phase transformations during the vacuum carburizing process caused by the ultra-high alloy content of 15Cr14Co12Mo5Ni2W steel, vacuum short-term intense carburization and diffusion experiments were designed. Characterization methods such as XRD, scanning electron microscopy, electron probe microanalysis, EBSD, and FIB+TEM were used to study the microstructure, phase composition, phase constituents, and distribution of element concentrations during the vacuum carburizing process. Subsequently, a carbon concentration distribution calculation model considering carbide changes during the vacuum carburizing process of 15Cr14Co12Mo5Ni2W steel was established. Boundary conditions for carbon absorption during the intense carburization process of 15Cr14Co12Mo5Ni2W steel were proposed, and the relationship between surface carbon concentration and boundary conditions was determined through experiments. Using the method of forward calculation with least squares fitting of experimental data, the diffusion coefficient of carbon in the austenite of 15Cr14Co12Mo5Ni2W steel and the rate of carbon exchange between carbides and the matrix were derived, providing a more precise predictive tool for optimizing the vacuum carburizing process of 15Cr14Co12Mo5Ni2W steel.

Influence of the solar penetration depth and heat-fluxes on the sea surface temperature using an ocean mixed layer model

Solar penetration depth is a major factor in modulating the Sea Surface Temperature (SST) and hence influences the air-sea interface processes. This study investigates the influence of solar penetration depth and heat-fluxes on the SST at three RAMA (Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction) buoy locations (along 90°E at 15°N, 12°N, and 8°N) in the Bay of Bengal (BoB) using the One-Dimensional Price-Weller-Pinkel (1D-PWP) model. The results show that SST has strong dependence on the solar penetration depths with the shortwave component of solar insolation outweighing its longwave component. We found that spatio-temporally varying penetration depths refine the simulated SST, when compared against constant penetration depths. These spatio-temporally varying penetration depths (ranging from 10 to 75 m for the shortwave component; with higher values during monsoon) can be attributed to spatio-temporally changing biological parameters, wind, precipitation, and suspended particulate matter. Upon examination, an antiphase relationship between the penetration depths and total chlorophyll, total primary production, & total phytoplankton was found. Upon including the effect of horizontal advection and vertical entrainment in the 1D-PWP model results, the SST predictions improved albeit by a small amount. Furthermore, the effects of increased heat-fluxes, under the different Representative Concentration Pathway (RCP) equivalent scenarios, coupled with varying penetration depths were estimated. It was found that for the spatio-temporally varying penetration depths, the mean (maximum) increment was 0.52 °C (0.92 °C) under the highest emission scenario, i.e., RCP8.5, across all the locations. The results of the study can potentially be used further for 3D ocean modeling studies, and the characterization of air-sea interaction during monsoon and cyclones in the BoB.

Knowledge-based product and process design synthesis of additively manufactured non-assembly mechanisms

Additive Manufacturing increasingly becomes a viable alternative to conventional manufacturing technologies. However, the knowledge and awareness to achieve a ready-for-manufacturing and quality-ensured design of additively manufactured products are mostly lacking. Knowledge from the manufacturing process on how certain process variables affect the product requirements must already be considered in product design to assure and potentially enhance the quality a priori, thus saving time and cost-intensive iterations. Therefore, this knowledge must be front-loaded as a basis to suitably adapt product and process design simultaneously. This contribution therefore proposes a framework for the simultaneous product and process design synthesis of existing designs by providing insight knowledge about product and process design specific to Fused Layer Modeling. This is achieved via a consolidated ontology-based knowledge representation and its integration in a design environment. Thus, the aim, in contrast to most existing approaches, is to provide this knowledge on a product- and application-specific basis to parallelise product and process design for non-assembly mechanisms and exploit currently unused potential concerning the interaction between the different domains. This can shorten development times and also compensate for a lack of background knowledge in certain areas. A practical use case highlights the general procedure, benefits, and applicability.

Fracture behavior of composite piezoelectric intelligent structures formed by pasting MFC based on serial multi-scale method

The composite piezoelectric intelligent structures formed by combining piezoelectric materials with composite materials can achieve state perception and deformation control of the structure. Based on the molecular dynamics method, the continuum finite element method and the cohesive force model, the serial multi-scale research on the bonded composite piezoelectric intelligent structure was carried out from the micro, meso and macro scales. Molecular models of adhesive, macro fiber composite (MFC) surface and composite surface were established. The effects of temperature and pressure on the surface interaction energy of adhesive/MFC and adhesive/composite materials were analyzed. The traction-separation (T-S) relationship of cohesive region was obtained by virtual stretching method. Combined with the microscopic observation of the mesoscopic adhesive layer, the representative volume element (RVE) model of the mesoscopic adhesive layer considering the bubble defect was established, and the cohesive relationship curve of the adhesive layer was obtained. A double cantilever beam (DCB) macro finite element model was established to simulate the crack propagation behavior of the adhesive layer, and compared with the experimental results.

Evaluation and development of surface layer scheme representation of temperature inversions over boreal forests in Arctic wintertime conditions

Abstract. In this study, the Noah land surface model used in conjunction with the Mellor–Yamada–Janjić surface layer scheme (hereafter, Noah-MYJ) and the Noah multiphysics scheme (Noah-MP) from the Weather Research and Forecasting (WRF) 4.5.1 mesoscale model are evaluated with regard to their performance in reproducing positive temperature gradients over forested areas in the Arctic winter. First, simplified versions of the WRF schemes, recoded in Python, are compared with conceptual models of the surface layer in order to gain insight into the dependence of the temperature gradient on the wind speed at the top of the surface layer. It is shown that the WRF schemes place strong limits on the turbulent collapse, leading to lower surface temperature gradient at low wind speeds than in the conceptual models. We implemented modifications to the WRF schemes to correct this effect. The original and modified versions of Noah-MYJ and Noah-MP are then evaluated compared to long-term measurements at the Ameriflux Poker Flat Research Range, a forest site in interior Alaska. Noah-MP is found to perform better than Noah-MYJ because the former is a two-layer model which explicitly takes into account the effect of the forest canopy. Indeed, a non-negligible temperature gradient is maintained below the canopy at high wind speeds, leading to overall larger gradients than in the absence of vegetation. Furthermore, the modified versions are found to perform better than the original versions of each scheme because they better reproduce strong temperature gradients at low wind speeds.

Optical Dispersion Data Analysis of Single‐Crystal CH3NH3PbBr3 for Optimized Perovskite Solar Cell Active Layer Absorptance

Proper derivation of CH3NH3PbX3 (MAPbX3; where X = Cl−, Br−, I−) optical constants is a critical step toward the development of high‐performance perovskite devices. To date, the optical dispersions at all wavelengths have been inconsistently characterized by under‐approximating or omitting anomalous spectral features. Herein, a rigorous optical dispersion data analysis of single‐crystal MAPbBr3 involving variable‐angle spectroscopic ellipsometry data appended with transmission intensity data is presented. This approach yields a more robust derivation of the refractive index and extinction coefficient for both anomalous (absorptance) and normal (no absorptance) optical dispersion regimes. Using the derived optical constants, illustrative modeled perovskite solar cell device designs are presented in relation to nonrealistic designs prepared using representative optical constants reported in the literature. In comparison, the derived optical constants enables the modeling of layer thicknesses to maximize absorption by the active layer (MAPbBr3) and minimize parasitic optical absorptance by the nonactive layers at broad angles of incidence (≈0°–70°). This robust derivation of MAPbBr3 optical constants is expected to impact the optical dispersion data analysis of all perovskite analogs and expedite targeted development of, for example, solar cell, light‐emitting diode, photo‐ and X‐ray/γ‐ray detector, and laser system technologies.

Nonlinear Wave Damping by Kelvin–Helmholtz Instability-induced Turbulence

Magnetohydrodynamic kink waves naturally form as a consequence of perturbations to a structured medium, for example, transverse oscillations of coronal loops. Linear theory has provided many insights into the evolution of linear oscillations, and results from these models are often applied to infer information about the solar corona from observed wave periods and damping times. However, simulations show that nonlinear kink waves can host the Kelvin–Helmholtz instability (KHI), which subsequently creates turbulence in the loop, dynamics that are beyond linear models. In this paper we investigate the evolution of KHI-induced turbulence on the surface of a flux tube where a nonlinear fundamental kink mode has been excited. We control our numerical experiment so that we induce the KHI without exciting resonant absorption. We find two stages in the KHI turbulence dynamics. In the first stage, we show that the classic model of a KHI turbulent layer growing at ∝t is applicable. We adapt this model to make accurate predictions of the damping of the oscillation and turbulent heating as a consequence of the KHI dynamics. In the second stage, the now dominant turbulent motions are undergoing decay. We find that the classic model of energy decay proportional to t −2 approximately holds and provides an accurate prediction of the heating in this phase. Our results show that we can develop simple models for the turbulent evolution of a nonlinear kink wave, but the damping profiles produced are distinct from those of linear theory that are commonly used to confront theory and observations.

Equivalent single-layer model for hierarchical diamond honeycomb sandwich panels using variational asymptotic method

Sandwich panels with a hierarchical core facilitate the optimal utilization of limited materials to enhance bearing capacity in strategic locations. Nevertheless, the complex sub-structural cells have hindered extensive investigation into their static and modal characteristics. The objective of this study is to obtain the effective plate properties by homogenizing the representative unit cell and subsequently applying them to a two-dimensional equivalent single layer model (2D-ESL) based on the variational asymptotic method (VAM). Furthermore, three-point bending test of 3D-printed specimen and 3D detailed finite element results are employed to confirm the predictions of 2D-ESL under various load and boundary conditions. The effects of material factors (such as fiber volume fraction and layup configuration) and structural parameters (including aspect ratio, hierarchy ratio, and slenderness ratios of ribs and ligaments) on the equivalent stiffness and modal characteristics are also examined. Moreover, sandwich panels with different hierarchical configurations (including 2, 3 and infinite segments within the inclined ribs) are investigated to provide valuable insights for selecting appropriate hierarchies based on specific conditions. Notably, the transition from polyline to arc-shaped segments has been proven to effectively mitigate local stress concentration, leading to a more uniform distribution of local displacement.