Thermoelectric enhancement in triple-doped strontium titanate with multi-scale microstructure

Strontium titanate(SrTiO_(3))is a thermoelectric material with large Seebeck coefficient that has potential applications in high-temperature power generators.To simultaneously achieve a low thermal conductivity and high electrical conductivity,polycrystalline SrTiO_(3)with a multi-scale architecture was designed by the co-doping with lanthanum,cerium,and niobium.High-quality nano-powders were synthesized via a hydrothermal method.Nano-inclusions and a nano/micro-sized second phase precipitated during sintering to form mosaic crystal-like and epitaxial-like structures,which decreased the thermal conductivity.Substituting trivalent Ce and/or La with divalent Sr and substituting pentavalent Nb with tetravalent Ti enhanced the electrical conductivity without decreasing the Seebeck coefficient.By optimizing the dopant type and ratio,a low thermal conductivity of 2.77 W·m^(-1)·K^(-1)and high PF of 1.1 mW·m^(-1)·K^(-2)at 1000 K were obtained in the sample co-doped with 5-mol%La,5-mol%Ce,and 5-mol%Nb,which induced a large ZT of 0.38 at 1000 K.

A semi-analytical model for coupled flow in stress-sensitive multi-scale shale reservoirs with fractal characteristics

A large number of nanopores and complex fracture structures in shale reservoirs results in multi-scale flow of oil. With the development of shale oil reservoirs, the permeability of multi-scale media undergoes changes due to stress sensitivity, which plays a crucial role in controlling pressure propagation and oil flow. This paper proposes a multi-scale coupled flow mathematical model of matrix nanopores, induced fractures, and hydraulic fractures. In this model, the micro-scale effects of shale oil flow in fractal nanopores, fractal induced fracture network, and stress sensitivity of multi-scale media are considered. We solved the model iteratively using Pedrosa transform, semi-analytic Segmented Bessel function, Laplace transform. The results of this model exhibit good agreement with the numerical solution and field production data, confirming the high accuracy of the model. As well, the influence of stress sensitivity on permeability, pressure and production is analyzed. It is shown that the permeability and production decrease significantly when induced fractures are weakly supported. Closed induced fractures can inhibit interporosity flow in the stimulated reservoir volume (SRV). It has been shown in sensitivity analysis that hydraulic fractures are beneficial to early production, and induced fractures in SRV are beneficial to middle production. The model can characterize multi-scale flow characteristics of shale oil, providing theoretical guidance for rapid productivity evaluation.

Relationship between the unique microstructures and behaviors of high-entropy alloys

High-entropy alloys(HEAs),which were introduced as a pioneering concept in 2004,have captured the keen interest of nu-merous researchers.Entropy,in this context,can be perceived as representing disorder and randomness.By contrast,elemental composi-tions within alloy systems occupy specific structural sites in space,a concept referred to as structure.In accordance with Shannon entropy,structure is analogous to information.Generally,the arrangement of atoms within a material,termed its structure,plays a pivotal role in dictating its properties.In addition to expanding the array of options for alloy composites,HEAs afford ample opportunities for diverse structural designs.The profound influence of distinct structural features on the exceptional behaviors of alloys is underscored by numer-ous examples.These features include remarkably high fracture strength with excellent ductility,antiballistic capability,exceptional radi-ation resistance,and corrosion resistance.In this paper,we delve into various unique material structures and properties while elucidating the intricate relationship between structure and performance.

Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions

Multi-scale system remains a classical scientific problem in fluid dynamics,biology,etc.In the present study,a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data.The flow is divided into several regions with different scales based on Prandtl's boundary theory.Different regions are solved with governing equations in different scales.The method of matched asymptotic expansions is used to make the flow field continuously.A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale.The results are compared with the reference numerical solutions,which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows.This scheme can be developed for more multi-scale problems in the future.

Microstructure and Properties of AlCoCrFeNiTi High-Entropy Alloy Coatings Prepared by Laser Cladding

21-4N(5Cr21Mn9Ni4N)is extensively employed in the production of engine valves,operating under severe conditions.Apart from withstanding high-temperature gas corrosion,it must also endure the impact of cylinder explosion pressure.The predominant failure mode of 21-4N valves is abrasive wear.Surface coatings serve as an effective approach to prevent such failures.In this investigation,Laser cladding technology was utilized to fabricate AlCoCrFeNiTi high entropy alloy coatings onto the surfaces of 21-4N valves.According to the findings,the cladding zone has a normal dendritic microstructure,a good substrate-to-cladding layer interaction,and no obvious flaws.In terms of hardness,the cladding demonstrates an average hardness of 620 HV.The hardness has increased by 140%compared to the substrate.The average hardness of the cladding remains at approximately 520 HV even at elevated temperatures.Regarding frictional wear performance,between 400℃and 800℃,the cladding layer exhibits an average friction coefficient of 0.4,with the primary wear mechanisms being abrasive wear,adhesive wear,and a minor degree of plastic deformation.

Microstructures,corrosion behavior and mechanical properties of as-cast Mg-6Zn-2X(Fe/Cu/Ni)alloys for plugging tool applications

Mg-6Zn-2X(Fe/Cu/Ni)alloys were prepared through semi-continuous casting,with the aim of identifying a degradable magnesium(Mg)alloy suitable for use in fracturing balls.A comparative analysis was conducted to assess the impacts of adding Cu and Ni,which result in finer grains and the formation of galvanic corrosion sites.Scanner electronic microscopy examination revealed that precipitated phases concentrated at grain boundaries,forming a semi-continuous network structure that facilitated corrosion penetration in Mg-6Zn-2Cu and Mg-6Zn-2Ni alloys.Pitting corrosion was observed in Mg-6Zn-2Fe,while galvanic corrosion was identified as the primary mechanism in Mg-6Zn-2Cu and Mg-6Zn-2Ni alloys.Among the tests,the Mg-6Zn-2Ni alloy exhibited the highest corrosion rate(approximately 932.9 mm/a)due to its significant potential difference.Mechanical testing showed that Mg-6Zn-2Ni alloy possessed suitable ultimate compressive strength,making it a potential candidate material for degradable fracturing balls,effectively addressing the challenges of balancing strength and degradation rate in fracturing applications.

Microstructures and micromechanical behaviors of high -entropy alloys investigated by synchrotron X-ray and neutron diffraction techniques: A review

High-entropy alloys(HEAs)possess outstanding features such as corrosion resistance,irradiation resistance,and good mechan-ical properties.A few HEAs have found applications in the fields of aerospace and defense.Extensive studies on the deformation mech-anisms of HEAs can guide microstructure control and toughness design,which is vital for understanding and studying state-of-the-art structural materials.Synchrotron X-ray and neutron diffraction are necessary techniques for materials science research,especially for in situ coupling of physical/chemical fields and for resolving macro/microcrystallographic information on materials.Recently,several re-searchers have applied synchrotron X-ray and neutron diffraction methods to study the deformation mechanisms,phase transformations,stress behaviors,and in situ processes of HEAs,such as variable-temperature,high-pressure,and hydrogenation processes.In this review,the principles and development of synchrotron X-ray and neutron diffraction are presented,and their applications in the deformation mechanisms of HEAs are discussed.The factors that influence the deformation mechanisms of HEAs are also outlined.This review fo-cuses on the microstructures and micromechanical behaviors during tension/compression or creep/fatigue deformation and the application of synchrotron X-ray and neutron diffraction methods to the characterization of dislocations,stacking faults,twins,phases,and intergrain/interphase stress changes.Perspectives on future developments of synchrotron X-ray and neutron diffraction and on research directions on the deformation mechanisms of novel metals are discussed.

Microstructure and damping properties of LPSO phase dominant Mg-Ni-Y and Mg-Zn-Ni-Y alloys

This work studied the microstructure,mechanical properties and damping properties of Mg_(95.34)Ni_(2)Y_(2.66) and Mg_(95.34)Zn_(1)Ni_(1)Y_(2.66)alloys systematically.The difference in the evolution of the long-period stacked ordered(LPSO)phase in the two alloys during heat treatment was the focus.The morphology of the as-cast Mg_(95.34)Ni_(2)Y_(2.66)presented a disordered network.After heat treatment at 773 K for 2 hours,the eutectic phase was integrated into the matrix,and the LPSO phase maintained the 18R structure.As Zn partially replaced Ni,the crystal grains became rounded in the cast alloy,and lamellar LPSO phases and more solid solution atoms were contained in the matrix after heat treatment of the Mg_(95.34)Zn_(1)Ni_(1)Y_(2.66)alloy.Both Zn and the heat treatment had a significant effect on damping.Obvious dislocation internal friction peaks and grain boundary internal friction peaks were found after temperature-dependent damping of the Mg_(95.34)Ni_(2)Y_(2.66)and Mg_(95.34)Zn_(1)Ni_(1)Y_(2.66)alloys.After heat treatment,the dislocation peak was significantly increased,especially in the alloy Mg_(95.34)Ni_(2)Y_(2).66.The annealed Mg_(95.34)Ni_(2)Y_(2.66)alloy with a rod-shaped LPSO phase exhibited a good damping performance of 0.14 atε=10^(−3),which was due to the difference between the second phase and solid solution atom content.These factors also affected the dynamic modulus of the alloy.The results of this study will help in further development of high-damping magnesium alloys.

Microstructure and water-swelling mechanism of red-bed mudstone in the Xining region,Northeastern Tibetan Plateau

This paper examines the effect of the microstructure and electrical conductivity(EC)on the swelling ratio and pressure in red-bed mudstone sampled from arid areas in the Xining region in the northeastern Tibetan Plateau.A series of laboratory tests,including swelling experiments,X-ray diffraction(XRD),and scanning electron microscope(SEM),was carried out for mechanical and microstructural analysis.The coupled influence of the EC and microstructural parameters on the expansion ratio and pressure was investigated,and the weight coefficients were discussed by the entropy weight method.The results revealed an increasing exponential trend in EC,and the maximum swelling speed occurred at an EC of approximately 10 μS/cm.In addition,a method for predicting the expansion potential is proposed based on the microstructure,and its reliability is verified by comparing with swelling experimental results.In addition,according to the image analysis results,the ranges of the change in the clay minerals content(CMC),the fractal dimension(FD),the average diameter(AD)of pores,and the plane porosity(PP)are 23.75%-53%,1.08-1.17,7.53-22.45 mm,and 0.62%-1.25%,respectively.Moreover,mudstone swelling is negatively correlated with the plane porosity,fractal dimension and average diameter and is linearly correlated with the clay mineral content.Furthermore,the weight values prove that the microstructural characteristics,including FD,AD,and PP,are the main factors influencing the expansion properties of red-bed mudstones in the Xining region.Based on the combination of macro and micro-analyses,a quantitative analysis of the swelling process of mudstones can provide a better reference for understanding the mechanism of expansion behavior.

Toward understanding the microstructure characteristics,phase selection and magnetic properties of laser additive manufactured Nd-Fe-B permanent magnets

Nd-Fe-B permanent magnets play a crucial role in energy conversion and electronic devices.The essential magnetic properties of Nd-Fe-B magnets,particularly coercivity and remanent magnetization,are significantly infuenced by the phase characteristics and microstructure.In this work,Nd-Fe-B magnets were manufactured using vacuum induction melting(VIM),laser directed energy deposition(LDED)and laser powder bed fusion(LPBF)technologies.Themicrostructure evolution and phase selection of Nd-Fe-B magnets were then clarified in detail.The results indicated that the solidification velocity(V)and cooling rate(R)are key factors in the phase selection.In terms of the VIM-casting Nd-Fe-B magnet,a large volume fraction of theα-Fe soft magnetic phase(39.7 vol.%)and Nd2Fe17Bxmetastable phase(34.7 vol.%)areformed due to the low R(2.3×10-1?C s-1),whereas only a minor fraction of the Nd2Fe14B hard magnetic phase(5.15 vol.%)is presented.For the LDED-processed Nd-Fe-B deposit,although the Nd2Fe14B hard magnetic phase also had a low value(3.4 vol.%)as the values of V(<10-2m s-1)and R(5.06×103?C s-1)increased,part of theα-Fe soft magnetic phase(31.7vol.%)is suppressed,and a higher volume of Nd2Fe17Bxmetastable phases(47.5 vol.%)areformed.As a result,both the VIM-casting and LDED-processed Nd-Fe-B deposits exhibited poor magnetic properties.In contrast,employing the high values of V(>10-2m s-1)and R(1.45×106?C s-1)in the LPBF process resulted in the substantial formation of the Nd2Fe14B hard magnetic phase(55.8 vol.%)directly from the liquid,while theα-Fe soft magnetic phase and Nd2Fe17Bxmetastable phase precipitation are suppressed in the LPBF-processed Nd-Fe-B magnet.Additionally,crystallographic texture analysis reveals that the LPBF-processedNd-Fe-B magnets exhibit isotropic magnetic characteristics.Consequently,the LPBF-processed Nd-Fe-B deposit,exhibiting a coercivity of 656 k A m-1,remanence of 0.79 T and maximum energy product of 71.5 k J m-3,achieved an acceptable magnetic performance,comparable to other additive manufacturing processed Nd-Fe-B magnets from MQP(Nd-lean)Nd-Fe-Bpowder.

Multi-scale context-aware network for continuous sign language recognition

The hands and face are the most important parts for expressing sign language morphemes in sign language videos.However,we find that existing Continuous Sign Language Recognition(CSLR)methods lack the mining of hand and face information in visual backbones or use expensive and time-consuming external extractors to explore this information.In addition,the signs have different lengths,whereas previous CSLR methods typically use a fixed-length window to segment the video to capture sequential features and then perform global temporal modeling,which disturbs the perception of complete signs.In this study,we propose a Multi-Scale Context-Aware network(MSCA-Net)to solve the aforementioned problems.Our MSCA-Net contains two main modules:(1)Multi-Scale Motion Attention(MSMA),which uses the differences among frames to perceive information of the hands and face in multiple spatial scales,replacing the heavy feature extractors;and(2)Multi-Scale Temporal Modeling(MSTM),which explores crucial temporal information in the sign language video from different temporal scales.We conduct extensive experiments using three widely used sign language datasets,i.e.,RWTH-PHOENIX-Weather-2014,RWTH-PHOENIX-Weather-2014T,and CSL-Daily.The proposed MSCA-Net achieve state-of-the-art performance,demonstrating the effectiveness of our approach.

MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot.However,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper.The main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale features.The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively.Secondly,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature information.Thirdly,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different directions.To verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried out.In the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,respectively.In the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,respectively.This model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia Computer-Aided Diagnosis.

YOLO-MFD:Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method.

Transfer learning framework for multi-scale crack type classification with sparse microseismic networks

Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.

Wetting-drying effect on the strength and microstructure of cementphosphogypsum stabilized soils

Phosphogypsum has often been used as an effective and environmentally friendly binder for partial replacement of cement,improving the engineering properties of slurries with high water content.However,the influence of phosphogypsum on the physicomechnical properties of stabilized soil subjected to wettingedrying cycles is not well understood to date.In this study,the effect of phosphogypsum on the durability of stabilized soil was studied by conducting a series of laboratory experiments,illustrating the changes in mass loss,pH value and unconfined compressive strength(qu)with wettingdrying cycles.The test results showed that the presence of phosphogypsum significantly restrained the mass loss in the early stage(lower than the 4th cycle),which in turn led to a higher qu of stabilized soil than that without phosphogypsum.After the 4th cycle,a sudden increase in mass loss was observed for stabilized soil with phosphogypsum,resulting in a significant drop in qu to a value lower than those without phosphogypsum at the 6th cycle.In addition,the qu of stabilized soils correlated well with the measured soil pH irrespective of phosphogypsum content for all wettingedrying tests.According to the microstructure observation via scanning electron microscope(SEM)and X-ray diffraction(XRD)tests,the mechanisms relating the sudden loss of qu for the stabilized soils with phosphogypsum after the 4th wetting-drying cycle are summarized as follows:(i)the disappearance of ettringite weakening the cementation bonding effect,(ii)the generation of a larger extent of microcrack,and(iii)a lower pH value,in comparison with the stabilized soil without phosphogypsum.

MSC-YOLO:Improved YOLOv7 Based on Multi-Scale Spatial Context for Small Object Detection in UAV-View

Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.

Combined Insights from Leachate Structure and Microstructure Characteristics for Eating Quality of Convenience Rice Processed by Super-Heated and Pressurized Steam Technologies

Convenience rice has become widely popular due to its easy availability for cooking. This study investigated the starch structure and composition of leachate and the microstructure of reheated convenience rice using novel processing technologies: super-heated steaming(SHS), auto-electric cooking(AEC), and pressurized-steam cooking(PSC). Additionally, the effect of two different target water contents(58% and 63%) was also evaluated. The PSC_63% sample had the highest total solids and amylopectin amount in the leachate. The amylopectin amount in the leachate differed significantly based on the targeted water content. Morphological characterization revealed that the swelling of starch and the coated layer on the surface of rice grains were most pronounced in the PSC_63% sample due to the pressure processing. The textural hardness of the AEC_58% sample was much higher than that of the other samples. The PSC_63% sample had the highest textural adhesiveness value, which can be attributed to the highest amylopectin amount in the leachate. Sensory characterization showed that the PSC_63% sample had the highest glossiness, whiteness, moistness, and overall acceptability. The principal component analysis score plots presented substantial differences in the leachate and textural and sensory characteristics of reheated convenience rice among the different processing technologies.

Influences of shale microstructure on mechanical properties and bedding fractures distribution

The difference in microstructure leads to the diversity of shale mechanical properties and bedding fractures distribution patterns.In this paper,the microstructure and mechanical properties of Longmaxi marine shale and Qingshankou continental shale were studied by X-ray diffractometer(XRD),field emission scanning electron microscope(FE-SEM)with mineral analysis system,and nanoindentation.Additionally,the typical bedding layers area was properly stratified using Focused Ion Beam(FIB),and the effects of microstructure and mechanical properties on the distribution patterns of bedding fractures were analyzed.The results show that the Longmaxi marine shale sample contains more clay mineral grains,while the Qingshankou continental shale sample contains more hard brittle mineral grains such as feldspar.For Longmaxi marine shale sample,hard brittle minerals with grain sizes larger than 20μm is18.24%and those with grain sizes smaller than 20μm is 16.22%.For Qingshankou continental shale sample,hard brittle minerals with grain sizes larger than 20μm is 40.7%and those with grain sizes smaller than 20μm is 11.82%.In comparison to the Qingshankou continental shale sample,the Longmaxi marine shale sample has a lower modulus,hardness,and heterogeneity.Laminated shales are formed by alternating coarse-grained and fine-grained layers during deposition.The average single-layer thickness of Longmaxi marine shale sample is greater than Qingshankou continental shale sample.The two types of shale have similar bedding fractures distribution patterns and fractures tend to occur in the transition zone from coarse-grained to fine-grained deposition.The orientation of the fracture is usually parallel to the bedding plane and detour occurs in the presence of hard brittle grains.The fracture distribution density of the Longmaxi marine shale sample is lower than that of the Qingshankou continental shale sample due to the strong heterogeneity of the Qingshankou continental shale.The current research provides guidelines for the effective development of shale reservoirs in various sedimentary environments.

Multi-scale Modeling and Finite Element Analyses of Thermal Conductivity of 3D C/SiC Composites Fabricating by Flexible-Oriented Woven Process

Thermal conductivity is one of the most significant criterion of three-dimensional carbon fiber-reinforced SiC matrix composites(3D C/SiC).Represent volume element(RVE)models of microscale,void/matrix and mesoscale proposed in this work are used to simulate the thermal conductivity behaviors of the 3D C/SiC composites.An entirely new process is introduced to weave the preform with three-dimensional orthogonal architecture.The 3D steady-state analysis step is created for assessing the thermal conductivity behaviors of the composites by applying periodic temperature boundary conditions.Three RVE models of cuboid,hexagonal and fiber random distribution are respectively developed to comparatively study the influence of fiber package pattern on the thermal conductivities at the microscale.Besides,the effect of void morphology on the thermal conductivity of the matrix is analyzed by the void/matrix models.The prediction results at the mesoscale correspond closely to the experimental values.The effect of the porosities and fiber volume fractions on the thermal conductivities is also taken into consideration.The multi-scale models mentioned in this paper can be used to predict the thermal conductivity behaviors of other composites with complex structures.

Microstructure and impact behavior of Mg-4Al-5RE-xGd cast magnesium alloys

This work investigated the microstructure and impact behavior of Mg-4Al-5RE-xGd(RE represents La-Ce mischmetal;x=0,0.2,0.7 wt.%)alloys cast by high-pressure die casting(HPDC),permanent mold casting(PMC),and sand casting(SC)techniques.The results indicated that with increasing Gd content,the grain sizes of the HPDC alloy had a slight change,but the grains of the PMC and SC alloys were significantly refined.Besides,the acicular Al_(11)RE_(3)phase was modified into the short-rod shape under the three casting conditions.The impact toughness of the studied alloy was mainly dominated by the absorbed energy during the crack initiation.With increasing Gd content,the impact toughness of the studied alloy monotonically increased due to the lower tendency of the modified second phase toward crack initiation.The impact stress was higher than the tensile stress,exhibiting a strain rate sensitivity for the mechanical response;however,the HPDC alloy had an inconsistent strain rate sensitivity during the impact event due to the transformation of the deformation mechanism from twinning to slip with increasing strain.Abundant dimples covered the fracture surface of the fine-grained HPDC alloys,indicating a typical ductile fracture.Nevertheless,due to the deficient{1012}twinning activity and the suppressed grain boundary sliding during the impact event,the HPDC alloys showed insufficient plastic deformation capacity.