Combined 3D Research Articles

The role of three dimension computed tomography in Lisfranc injury diagnosis

PurposeTo evaluate the intra/inter observer and diagnostic reliability of 3-dimensional (3D) model reconstruction computed tomography (CT) compared to the traditional two-dimensional (2D) CT when evaluating Lisfranc (LF) injuries. MethodsA retrospective study was performed on CT studies of patients with clinically suspected LF injuries examined in the emergency department at medium size medical center. Each CT study was evaluated for metatarsal fractures and subluxations employing both standard 2D and 3D CT model reconstruction. Four orthopaedists, 2 senior and 2 residents, were assigned to review and evaluate each CT. Each CT study was reviewed twice by each reader in a randomized order. Descriptive statistics were calculated for all measured variables. The intra-observer and inter-observer agreement Kappa coefficients were calculated to evaluate reliability and reproducibility between and within readers for each modality. ResultsThe study included 44 patients. Median age was 41.4 years (interquartile range, 23-58). The intra-observer and inter-observer reliability was good (intra-observer; 3D Kappa; 0.76 and 2D Kappa 0.73 p<0.001, inter-observer; 3D Kappa 0.68, 2D Kappa 0.63 and combined 2D and 3D kappa 0.68, p<0.001). Three dimensional CT was found to be more sensitive than 2D CT, specifically when evaluating for second metatarsal dislocation (sensitivity: 70% vs 47%). Combined evaluation of 2D and 3D CT, greatly improved sensitivity rate to 85.7%. In terms of fracture diagnosis, combined evaluation of 2D and 3D CT showed higher sensitivity and specificity rates as compared to 2D or 3D alone. ConclusionThe employment of 3D CT in LF injury diagnosis, in isolation and in combination with 2D CT, seems to improve the diagnostic accuracy and reliability between and within observers compared to 2D CT alone.

Correlation of 2D Electrical Resistivity and Self-Potential Methods for the Assessment of the Integrity of Goronyo Dam NW Nigeria

Abstract In this research, combined 2D electrical resistivity and self-potential (SP) methods were employed to locate anomalous electrical conductivity around the Goronyo dam in order to determine the condition of the embankment of the earth dam. The data were taken with the reference to resistivity and SP values using Wenner configuration. The results obtained from these techniques shows that the area was underlain by four stratigraphic layers, namely; clayey sand, wet sandy clay, laterite and partially weathered basement complex rock as compared with the resistivities of common rocks and borehole lithology of the area. The low SP (&lt; 200 mV)/high resistivity zones in the overburden of profiles P1, P2 and P3 shows the presence of partially weathered basement complex rocks which was probably due to compactness and dryness around the dam. However, the zones of high SP (≥ 200 mV)/low apparent resistivity regions (z1 and z2) of profiles P1 (2 Ωm to 27 Ωm), P2 (3 Ωm to 25 Ωm) and P3 (3 Ωm to 14 Ωm) have revealed the weak zones associated with wet sandy soil. Results obtained from 2D electrical resistivity (z1) technique have correlated well with the results of SP sections (z2). These regions of weak zones occupied the x-distance along the profiles; P1/or S2 (120 m to 200 m and 250 m to 400 m), P2/or S2 (1 m to 156 m and 192 m to 400 m) and P3/or S3 (48 m to 400 m) with their corresponding depth y, ranging from 23 m to 30 m, 26 m to 30 m and 23 m to 30 m. Keywords: 2D Electrical Resistivity Tomography (ERT), Self-Potential (SP), Dam, Subsurface, Seepage, Condition

The Growth Behavior for Intermetallic Compounds at the Interface of Aluminum-Steel Weld Joint.

In this work, the microstructure and growth behavior of Al-Fe intermetallic compounds (IMCs), which formed at interface of weld steel-aluminum joint, are successfully analyzed via the combination of experiment and physical model. A layer IMCs consists of Fe2Al5 and Fe4Al13, in which the Fe2Al5 is the main compound in the layer. The IMCs layer thickness increases with the increase of the heat input and the maximum thickness of IMCs layer is 22 ± 2 μm. The high vacancy concentration of Fe2Al5 IMCs provides the diffusion path for Al atoms to migrate through the IMCs layer for growing towards to steel substrate. By using the calculated temperature profiles as inputs, the combined 2D cellular automata (CA)-Monte Carlo (MC) model is applied to simulate the grain distribution and interfacial morphology evolution at the Al-steel interface. This 2D model simulates the IMCs nucleation, growth, and solute redistribution. The numerical results are in good agreement with the experimental results, suggesting that the growth process can be divided four stages, and the thickness of the Fe2Al5 layer increases nonlinearly with the increase of the growth time. The whole nucleation and growth process experienced 1.7~2 s, and the fastest growth rate is 8 μm/s. The addition of Si element will influence diffusion path of Al atom to form different interface morphology. The effects of peak temperature, cooling time, and the thermal gradient on the IMCs thickness are discussed. It shows that the peak temperature has the major influence on the IMCs thickness.

Combined experimental and numerical analysis of critical loading conditions for hard metal tool damage in titanium milling

The present work investigates an indexed cutter with round cutting inserts manufactured from hard metal grade M10 with 10 wt% Co binder and WC grain size of 2 μm used for the application of milling the titanium alloy Ti-6Al-4V. The focus is set on the cyclic thermomechanical loading in combination with tool degradation. For this sake, a combined 2D and 3D finite element modelling approach is introduced in order to determine the thermal and mechanical tool loading locally resolved and to reveal the specifics of a round cutting insert geometry. The finite element model allows establishing the linkage between the material damage observed in uniaxially loaded laboratory specimens with real tool degradation observed in cutting experiments for the calculated critical loading conditions. For the investigated round insert with 10 mm diameter applied in a cutting operation with 55 m min−1 cutting speed and 0.125 mm feed per tooth, a maximum cutting edge temperature between 600 °C and 700 °C and stress amplitudes between 1500MPa and 2000 MPa at a stress ratio Rσ < − 4 are calculated. This loading is close to the load limit for cavity formation and accumulation in M10 which for the temperature of 700 ° C and a stress ratio Rσ = − ∞ was experimentally found to be at a stress range of 1500 MPa. This reveals that the investigated process conditions are close to the limit of safe tool application which is in accordance with findings from cutting tests. The presented approach provides a novel and highly valuable method for future tool material and cutting process development and research.

In situ determination of the extreme damage resistance behavior in stomatopod dactyl club.

The structure and mechanical properties of the stomatopod dactyl club have been studied extensively for its extreme impact tolerance, but a systematic in situ investigation on the multiscale mechanical responses under high-speed impact has not been reported. Here the full dynamic deformation and crack evolution process within projectile-impacted dactyl using combined fast 2D X-ray imaging and high-resolution ex situ tomography are revealed. The results show that hydration states can lead to significantly different toughening mechanisms inside dactyl under dynamic loading. A previously unreported 3D interlocking structural design in the impact surface and impact region is reported using nano X-ray tomography. Experimental results and dynamic finite-element modeling suggest this unique structure plays an important role in resisting catastrophic structural damage and hindering crack propagation. This work is a contribution to understanding the key toughening strategies of biological materials and provides valuable information for biomimetic manufacturing of impact-resistant materials in general.

Depression Detection Using Deep Learning

The proportion of individuals with depression has rapidly increased along with the growth of the global population. Depression has been the currently most prevalent mental health disorder. An effective depression recognition system is especially crucial for the early detection of potential depression risk. A depression-related dataset is also critical while evaluating the system for depression or potential depression risk detection. Due to the sensitive nature of clinical data, the availability and scale of such datasets are scarce. Depression is classified as a mood disorder. It may be described as feelings of sadness, anger, or loss that interfere with a person’s everyday activities. People experience depression in different ways. In certain cases, depression may lead to fatal cases. To avoid all of these, depression must be detected at the earliest and victim must be treated with appropriate remedies. The objective of the project is to analyze the emotion of a user using real-time video. This is achieved using Convolutional Neural Networks [CNN]. The final decision result comes from the combination of the two models. Finally, we evaluate all proposed deep models on our built dataset. The experimental results demonstrate that (1) our proposed method can identify patients/users with potential depression risk; (2) the recognition performance of combined 2D and 3D features model outperforms using either 2D or 3D features model only; (3) the performance of depression recognition is higher in the positive and negative emotional stimulus. Meanwhile, we compare the performance with other methods on the same dataset.

Structural Architecture and Permeability Patterns of Crystalline Reservoir Rocks in the Northern Upper Rhine Graben: Insights from Surface Analogues of the Odenwald

Fracture network is a crucial element to address in any model of the thermo-hydro-mechanical behaviour of a reservoir rock. This study aims to provide quantified datasets and a further understanding of the critical parameters of the fracture network pattern in crystalline rocks. In the Northern Upper Rhine Graben, such rock units are targeted for multiple energy applications, from deep geothermal heat extraction to heat storage. Eleven outcrops were investigated with a combined LiDAR and 2D profiles analysis to extract faults and fracture network geometrical parameters, including length distribution, orientation, connectivity, and topology. These properties are used to decipher the structural architecture and estimate the flow properties of crystalline units. Fracture networks show a multi-scale power-law behaviour for length distribution. Fracture topology and orientation are mainly driven by both fault networks and lithology. Fracture apertures and permeability tensors were then calculated for two application case studies, including the stress field effect on aperture. Obtained permeabilities are in the range of those observed in the sub-surface in currently exploited reservoirs. The dataset provided in this study is thus suitable to be implemented in the modelling during the exploration stage of industrial applications involving fractured crystalline reservoirs.

Applying comparative molecular modelling techniques on diverse hydroxamate-based HDAC2 inhibitors: an attempt to identify promising structural features for potent HDAC2 inhibition

ABSTRACT Histone deacetylase 2 (HDAC2) has been implicated in a variety of cardiovascular and neurodegenerative disorders as well as in cancers. Thus, HDAC2 has become an exclusive target for anticancer drug development. Therefore, the development of newer HDAC2 inhibitors in disease conditions is a prime goal to restrain such a scenario. Although a handful of HDAC inhibitors was accepted for the treatment of HDAC-related disease conditions, the non-selective nature of these entities is one of the major setbacks in the treatment of specific HDAC isoform-related pathophysiology. In this framework, the analyses of pre-existing molecules are essential to identify the important structural features that can fulfil the requirements for the cap and linker moieties to obtain potent and effective HDAC2 inhibition. Thus, in this study, the implementation of a combined comparative 2D and 3D molecular modelling techniques was done on a group of 92 diverse hydroxamate derivatives having a wide range of HDAC2 inhibitory potency. Besides other crucial features, this study upheld the importance of groups like triazole and benzyl moieties along with the molecular fields that are crucial for regulating HDAC2 inhibition. The outcomes of this study may be employed for the designing of HDAC2 inhibitors in future.

Examples on Simulation Model

Abstract: A new methodology was developed Further real-time determination gate control operations of a river-reservoir system to minimize flooding conditions. The methodology is based upon an optimization-simulation model approach interfacing the genetic algorithm within simulation software for short-term rainfall forecasting, rainfall–runoff modeling (HEC-HMS), and a one-dimensional (1D), two-dimensional (2D), and combined 1D and 2D combined unsteady flow models (HEC-RAS). Both realtime rainfall data from next-generation radar (NEXRAD) and gaging stations, and forecasted rainfall are needed to make gate control decisions (reservoir releases) in real-time so that at timet, rainfall is known and rainfall over the future timeperiod(∆t)totimet+ ∆t can be forecasted. This new model can be used to manage reservoir release schedules (optimal gate operations) before, during, and after a rainfall event. Through real-time observations and optimal gate controls, downstream water surface elevations are controlled to avoid exceedance of threshold flood levels at target locations throughout a riverreservoir system to minimize the damage. In an example application, an actual river reach with a hypothetical upstream flood control reservoir is modeled in real-time to test the optimization-simulation portion of the overall model. Keywords: Simulation – Random numbers- Steps for simulation – Problems.

Characteristics of SACI combustion in a diesel rotary engine based on combined 1D and 3D comprehensive transient simulations

Wankel rotary engines (WREs) are widely used in aviation equipment, vehicles, micropower systems, etc. Diesel WREs are well suited for military and aerospace applications due to great safety features of the diesel fuel. In this paper, the transient in-chamber flow and combustion processes of a spark-assisted compression-ignition (SACI) direct injection diesel WRE were numerically simulated. Firstly, an effective method of using two rotating vectors is proposed to calculate the crank-angle-based instantaneous position of each location on the rotor surface in the rotor housing, which defines the law of dynamic boundary changes, to realize transient simulation. Secondly, the one-dimensional (1D) simulation results were used as the initial boundary conditions of the three-dimensional (3D) simulation, which improves the accuracy of the simulation and reduces the number of computational cycles during the validation of the 3D simulation model. Thirdly, model reduction and dynamic mesh control were used in the numerical simulation. The time of one calculation cycle was reduced from 223 h to 125 h, which is a 43.95% reduction. Finally, the 1D simulation revealed that ignoring the leakage between two adjacent combustion chambers was one of the main reasons for the error between the 3D simulated in-cylinder pressure and the experimental result. Based on the simulation methodology developed in this paper, the SACI combustion characteristics of a direct-injection diesel WRE were revealed. This paper provides an efficient simulation method for optimizing the design of novel WREs.

Galactic cosmic ray propagation through M dwarf planetary systems

ABSTRACT Quantifying the flux of cosmic rays reaching exoplanets around M dwarfs is essential to understand their possible effects on exoplanet habitability. Here, we investigate the propagation of Galactic cosmic rays as they travel through the stellar winds (astrospheres) of five nearby M dwarfs, namely: GJ 15A, GJ 273, GJ 338B, GJ 411, and GJ 887. Our selected stars each have one or two detected exoplanets and they all have wind mass-loss rates constrained by Lyman α observations. Our simulations use a combined 1D magnetohydrodynamic (MHD) Alfvén-wave-driven stellar wind model and 1D cosmic ray transport model. We find that GJ 411 and GJ 887 have Galactic cosmic rays fluxes comparable with Earth’s at their habitable zones. On the other hand, GJ 15A, GJ 273, and GJ 338B receive a lower Galactic cosmic ray flux in their habitable zones. All exoplanets in our sample, with exception of GJ 15A c and GJ 411 c, have a significantly lower flux of Galactic cosmic rays than values observed at the Earth because they orbit closer-in. The fluxes found here can be further used for chemical modelling of planetary atmospheres. Finally, we calculate the radiation dose at the surface of the habitable-zone planet GJ 273 b, assuming it has an Earth-like atmosphere. This planet receives up to 209 times less 15 MeV energy cosmic ray fluxes than values observed at Earth. However, for high-energy cosmic rays (∼GeV), the difference in flux is only 2.3 times smaller, which contributes to GJ 273 b receiving a significant surface radiation dose of 0.13 mSv yr−1 (40 per cent of the annual dose on Earth’s surface).

Screening Digital Breast Tomosynthesis: Radiation Dose Among Patients With Breast Implants.

To compare the mean glandular dose (MGD), cancer detection rate (CDR), and recall rate (RR) among screening examinations of patients with breast implants utilizing various digital breast tomosynthesis (DBT)-based imaging protocols. This IRB-approved retrospective study included 1998 women with breast implants who presented for screening mammography between December 10, 2013, and May 29, 2020. Images were obtained using various protocol combinations of DBT and 2D digital mammography. Data collected included MGD, implant type and position, breast density, BI-RADS final assessment category, CDR, and RR. Statistical analysis utilized type II analysis of variance and the chi-square test. The highest MGD was observed in the DBT only protocol, while the 2D only protocol had the lowest (10.29 mGy vs 5.88 mGy, respectively). Statistically significant difference in MGD was observed across protocols (P < 0.0001). The highest per-view MGD was among DBT full-field (FF) views in both craniocaudal and mediolateral oblique projections (P < 0.0001). No significant difference was observed in RR among protocols (P = 0.17). The combined 2D (FF only) + DBT implant-displaced (ID) views protocol detected the highest number of cancers (CDR, 7.2 per 1000), but this was not significantly different across protocols (P = 0.48). The combination of 2D FF views and DBT ID views should be considered for women with breast implants in a DBT-based screening practice when aiming to minimize radiation exposure without compromising the sensitivity of cancer detection. Avoidance of DBT FF in this patient population is recommended to minimize radiation dose.

Synthesis of Proposed Structure of Aaptoline A, a Marine Sponge-Derived 7,8-Dihydroxyquinoline, and Its Neuroprotective Properties in C. elegans.

A concise and efficient synthesis of the proposed structure of aaptoline A, a 7,8-dihydroxyquinoline derived from a marine sponge, was accomplished in seven steps with a 52% overall yield. A key feature of the synthesis is the high-yielding Ag(I)-catalyzed cycloisomerization of the N-propargylaniline precursor to afford the quinoline carboxylate skeleton from acid-labile methyl aminobenzoate. However, the spectral data of the synthesized aaptoline A were not consistent with those of previous studies. The structure of the synthesized aaptoline A was confirmed by combined 2D NMR analysis. Additional studies on the bioactivity of the synthesized aaptoline A revealed that it has the ability to protect dopaminergic neurons against MPP+-induced neurotoxicity in C. elegans. In addition, impaired food-sensing ability and travel distance capability in C. elegans were significantly ameliorated by aaptoline A treatment, suggesting that aaptoline A can protect dopaminergic neurons both morphologically and functionally.

Synthesis of Proposed Structure of Aaptoline B via Transition Metal-Catalyzed Cycloisomerization and Evaluation of Its Neuroprotective Properties in C. Elegans

A concise synthesis of the proposed structure of aaptoline B, a pyrroloquinoline derived from a marine sponge, was accomplished. A key feature of this synthesis is the versatile transition metal-catalyzed cycloisomerization of N-propargylaniline to construct a quinoline skeleton. However, the spectral data of the synthesized aaptoline B did not agree with those of previous studies. The structure of the synthesized aaptoline B was confirmed using a combined 2D NMR analysis. Furthermore, we assessed the possible neuroprotective potential of aaptoline B using the C. elegans model system. In this study, aaptoline B significantly improved the viability and the morphology of dopaminergic neurons of nematodes under MPP+ exposure conditions. We also found that MPP+-induced motor deficits in nematodes were efficiently restored by aaptoline B treatment. Our findings demonstrate the neuroprotective effects of aaptoline B against MPP+-induced dopaminergic neuronal damage. Further studies are underway to explain its pharmacological mechanism.

Inheritance of the reduced mitochondria of Giardia intestinalis is coupled to the flagellar maturation cycle

BackgroundThe presence of mitochondria is a distinguishing feature between prokaryotic and eukaryotic cells. It is currently accepted that the evolutionary origin of mitochondria coincided with the formation of eukaryotes and from that point control of mitochondrial inheritance was required. Yet, the way the mitochondrial presence has been maintained throughout the eukaryotic cell cycle remains a matter of study. Eukaryotes control mitochondrial inheritance mainly due to the presence of the genetic component; still only little is known about the segregation of mitochondria to daughter cells during cell division. Additionally, anaerobic eukaryotic microbes evolved a variety of genomeless mitochondria-related organelles (MROs), which could be theoretically assembled de novo, providing a distinct mechanistic basis for maintenance of stable mitochondrial numbers. Here, we approach this problem by studying the structure and inheritance of the protist Giardia intestinalis MROs known as mitosomes.ResultsWe combined 2D stimulated emission depletion (STED) microscopy and focused ion beam scanning electron microscopy (FIB/SEM) to show that mitosomes exhibit internal segmentation and conserved asymmetric structure. From a total of about forty mitosomes, a small, privileged population is harnessed to the flagellar apparatus, and their life cycle is coordinated with the maturation cycle of G. intestinalis flagella. The orchestration of mitosomal inheritance with the flagellar maturation cycle is mediated by a microtubular connecting fiber, which physically links the privileged mitosomes to both axonemes of the oldest flagella pair and guarantees faithful segregation of the mitosomes into the daughter cells.ConclusionInheritance of privileged Giardia mitosomes is coupled to the flagellar maturation cycle. We propose that the flagellar system controls segregation of mitochondrial organelles also in other members of this supergroup (Metamonada) of eukaryotes and perhaps reflects the original strategy of early eukaryotic cells to maintain this key organelle before mitochondrial fusion-fission dynamics cycle as observed in Metazoa was established.

The Earth-like Galactic cosmic ray intensity in the habitable zone of the M dwarf GJ 436

ABSTRACT Galactic cosmic rays are energetic particles important in the context of life. Many works have investigated the propagation of Galactic cosmic rays through the Sun’s heliosphere. However, the cosmic ray fluxes in M dwarf systems are still poorly known. Studying the propagation of Galactic cosmic rays through the astrospheres of M dwarfs is important to understand the effect on their orbiting planets. Here, we focus on the planetary system GJ 436. We perform simulations using a combined 1D cosmic ray transport model and 1D Alfvén-wave-driven stellar wind model. We use two stellar wind set-ups: one more magnetically dominated and the other more thermally dominated. Although our stellar winds have similar magnetic field and velocity profiles, they have mass-loss rates two orders of magnitude different. Because of this, they give rise to two different astrosphere sizes, one 10 times larger than the other. The magnetically dominated wind modulates the Galactic cosmic rays more at distances $\lt 0.2\,$ au than the thermally dominated wind due to a higher local wind velocity. Between 0.2 and 1 au the fluxes for both cases start to converge. However, for distances $\gt 10\,$ au, spatial diffusion dominates, and the flux of GeV cosmic rays is almost unmodulated. We find, irrespective of the wind regime, that the flux of Galactic cosmic rays in the habitable zone of GJ 436 (0.2–0.4 au) is comparable with intensities observed at Earth. On the other hand, around GJ 436 b (0.028 au), both wind regimes predict Galactic cosmic ray fluxes that are approximately 104 times smaller than the values observed at Earth.

Testing of an Optimization-Simulation Model for Real-Time Flood Operation of River-Reservoir Systems

A new methodology was developed for the real-time determination gate control operations of a river-reservoir system to minimize flooding conditions. The methodology is based upon an optimization-simulation model approach interfacing the genetic algorithm within MATLAB with simulation software for short-term rainfall forecasting, rainfall–runoff modeling (HEC-HMS), and a one-dimensional (1D), two-dimensional (2D), and combined 1D and 2D combined unsteady flow models (HEC-RAS). Both real-time rainfall data from next-generation radar (NEXRAD) and gaging stations, and forecasted rainfall are needed to make gate control decisions (reservoir releases) in real-time so that at time t, rainfall is known and rainfall over the future time-period (Δt) to time t + Δt can be forecasted. This new model can be used to manage reservoir release schedules (optimal gate operations) before, during, and after a rainfall event. Through real-time observations and optimal gate controls, downstream water surface elevations are controlled to avoid exceedance of threshold flood levels at target locations throughout a river-reservoir system to minimize the damage. In an example application, an actual river reach with a hypothetical upstream flood control reservoir is modeled in real-time to test the optimization-simulation portion of the overall model.

Deep Neural Networks for Depression Recognition Based on 2D and 3D Facial Expressions Under Emotional Stimulus Tasks.

The proportion of individuals with depression has rapidly increased along with the growth of the global population. Depression has been the currently most prevalent mental health disorder. An effective depression recognition system is especially crucial for the early detection of potential depression risk. A depression-related dataset is also critical while evaluating the system for depression or potential depression risk detection. Due to the sensitive nature of clinical data, availability and scale of such datasets are scarce. To our knowledge, there are few extensively practical depression datasets for the Chinese population. In this study, we first create a large-scale dataset by asking subjects to perform five mood-elicitation tasks. After each task, subjects' audio and video are collected, including 3D information (depth information) of facial expressions via a Kinect. The constructed dataset is from a real environment, i.e., several psychiatric hospitals, and has a specific scale. Then we propose a novel approach for potential depression risk recognition based on two kinds of different deep belief network (DBN) models. One model extracts 2D appearance features from facial images collected by an optical camera, while the other model extracts 3D dynamic features from 3D facial points collected by a Kinect. The final decision result comes from the combination of the two models. Finally, we evaluate all proposed deep models on our built dataset. The experimental results demonstrate that (1) our proposed method is able to identify patients with potential depression risk; (2) the recognition performance of combined 2D and 3D features model outperforms using either 2D or 3D features model only; (3) the performance of depression recognition is higher in the positive and negative emotional stimulus, and females' recognition rate is generally higher than that for males. Meanwhile, we compare the performance with other methods on the same dataset. The experimental results show that our integrated 2D and 3D features DBN is more reasonable and universal than other methods, and the experimental paradigm designed for depression is reasonable and practical.

Robust topological edge states from one-dimensional diatomic chain photonic crystals

The Su–Schrieffer–Heeger (SSH) model can occur in a one-dimensional (1D) diatomic chain photonic crystal (PC) in which a unit cell includes two same slabs (atoms). With different intervals of the two slabs, the two combined 1D PCs can support topological edge states in all photonic boundary bandgaps. These topological edge states come from the inversion of topological phase of the bands through the band folding effect. When the sum of the two atom intervals in the two different 1D PCs equals to the unit cell length, these edge state frequencies keep invariant.

Failure prediction of marine non-planar composite π joints based on combined 2D material model and progressive damage method

Non-planar composite π joints are receiving increasing attention in marine engineering. Efficient two-dimensional (2D) finite element (FE) methods, which can accurately predicted the failure behavior of this kind of joint, are critically meaningful for structural design emphasizing low computational time and easy operation. A material model is developed here, from which the equivalent engineering constants of plies with various angles can be obtained. Based on the material model, a 2D FE model is built to determine distributions of the shear and peel strain in a single-lap adhesively bonded joint. The strain distribution results from the 2D FE model agree well with experimental results, validating the accuracy of the material model. In addition, 2D FE modelling of the non-planar composite π joint under tensile loading is developed by combining the proposed material model and progressive damage method. The damage behavior and ultimate failure load are predicted and good agreements present between the numerical and experimental results. Totally, the reduced computational method combining the 2D FE model and progressive damage method is efficient and simple, can provide an accurate simulation of the damage behavior of complex composite bonded structures and has good prospects for engineering applications.