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This paper presents a comparative analysis of fiber aspect ratios using scanning electron microscopy (SEM) and the mean field homogenization approach. The novelty of this work lies in an effective fiber length evaluation based on a comparative analysis of fiber aspect ratios using scanning electron microscopy (SEM) and the mean field homogenization approach. This makes it possible to use an electron microscope to image fiber samples corresponding to the sample size using microtomography. Molded samples and pellets of four polyamide-6 short-glass fiber-reinforced composites with mass fractions of 15%, 30%, and 50% were considered. The aspect ratio distribution measured by SEM for the investigated materials was 20.25 with a coefficient of variation of 5.1%. The fiber aspect ratio obtained based on mean field homogenization theory and the tensile curve approximation was underestimated at 13.698 with a coefficient of variation of 5.2%. The deviation between the micro- and macro-estimates can be represented as a mean effective aspect ratio of 68% with a coefficient of variation of 8.5%. The developed technology for preparing samples for SEM and automated image processing can be used to study other short-reinforced polymer composite materials. The obtained estimates can serve as a useful reference when calibrating other models of short-fiber-reinforced polymer materials.

Computer vision technology for the automatic recognition and geometric characterization of carbon and glass fibers in scanning electron microscopy images is proposed. The proposed pipeline, combining the SAM model and DeepLabV3+, provides the generalizability and accuracy of the foundational SAM model and the ability to quickly train on a small amount of data via the DeepLabV3+ model. The pipeline was trained several times more rapidly with lower requirements for computing resources than fine-tuning the SAM model, with comparable inference time. On the basis of the pipeline, an end-to-end technology for processing images of electron microscopic fibers was developed, the input of which is images with metadata and the output of which is statistics on the distribution of the geometric characteristics of the fibers. This innovation is of great practical importance for modeling the physical characteristics of materials. This paper proposes a few-shot training procedure for the DeepLabV3+/SAM pipeline, combining the training of the DeepLabV3+ model weights and the SAM model parameters. It allows effective training of the pipeline using only 37 real labeled images. The pipeline was then adapted to a new type of fiber and background using 15 additional real labeled images. This article also proposes a method for generating synthetic data for training neural network models, which improves the quality of segmentation by the IoU and PixAcc metrics from 0.943 and 0.949 to 0.953 and 0.959, i.e., by 1% on average. The developed pipeline significantly reduces the time required to evaluate fiber length in scanning electron microscope images.

This paper discusses an approach to estimating the parameters of the cohesive zone model (CZM) by mode II by extruding the bushing along the lug axis. This method of evaluation requires small samples, which is particularly relevant when investigating short fiber-reinforced polymers (SFRPs) with additively manufactured embedded elements. Adhesion is investigated on the example of 30% carbon fiber-reinforced polyamide-6 molded to Ti-6Al-4V (VT6) selective laser-melted (SLM) alloy bushing in cases of a roughness Ra = 2.66 μm (vibratory finishing), Ra = 8.79 μm (sandblasting), and Ra = 10.02 (directly from SLM). The values of the maximum equivalent tangential contact stress were in a range from 1.1 MPa to 9.5 MPa, while the critical fracture energy for tangential slip was estimated at 15 N/mm for all cases. Experimental validation of the obtained CZM mode II was carried out by evaluating the load-carrying capacity of the lugs with different bushings. In both the experiment and the calculation, greater bushing roughness provides greater lug load-bearing capacity. The ribbed bushings added significant strength in the experiments, which confirmed the importance of considering the tangential mode in the contact model. The presented models can be used for the preliminary evaluation of short fiber-reinforced polyamide-6 parts with titanium-embedded elements bearing capacity.

A fully developed turbulent flow in a concentric annulus, Re =10,000, ri/ro=0.5, with an inner rotating cylinder in the velocity range N=Uω/Ub=0÷4, is studied via a large-eddy simulation. Also, for comparison, simulations by steady-state, unstatiounary RANS k-ω SST (URANS), and Elliptic Blending Model (EBM) were made. The main focus of this study is on the effect of high rotation on the mean flow, turbulence statistics, and vortex structure. Distribution of the tangential velocity and the Reynolds stress tensor change their behaviour at N>0.5∼1. With rotation increases, the production of tangential fluctuation becomes dominant over axial ones and the position of turbulent kinetic energy maximum shifts towards the wall into the buffer zone. URANS and EBM approaches show good agreement with LES in mean flow, turbulent statistics, and integral parameters. The difference in pressure loss prediction between LES and URANS does not exceed 20%, but the average difference is about 11%. The EBM approach underestimates pressure losses up to 9% and on average not more than 5%. Vortex structures are described well by URANS.

Computer vision technology for the automatic recognition and geometric characterization of carbon and glass fibers in scanning electron microscopy images is proposed. The proposed pipeline, combining the SAM model and DeepLabV3+, provides the generalizability and accuracy of the foundational SAM model and the ability to quickly train on a small amount of data via the DeepLabV3+ model. The pipeline was trained several times more rapidly with lower requirements for computing resources than fine-tuning the SAM model, with comparable inference time. On the basis of the pipeline, an end-to-end technology for processing images of electron microscopic fibers was developed, the input of which is images with metadata and the output of which is statistics on the distribution of the geometric characteristics of the fibers. This innovation is of great practical importance for modeling the physical characteristics of materials. This paper proposes a few-shot training procedure for the DeepLabV3+/SAM pipeline, combining the training of the DeepLabV3+ model weights and the SAM model parameters. It allows effective training of the pipeline using only 37 real labeled images. The pipeline was then adapted to a new type of fiber and background using 15 additional real labeled images. This article also proposes a method for generating synthetic data for training neural network models, which improves the quality of segmentation by the IoU and PixAcc metrics from 0.943 and 0.949 to 0.953 and 0.959, i.e., by 1% on average. The developed pipeline significantly reduces the time required to evaluate fiber length in scanning electron microscope images. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of fiber aspect ratios using scanning electron microscopy (SEM) and the mean field homogenization approach. The novelty of this work lies in an effective fiber length evaluation based on a comparative analysis of fiber aspect ratios using scanning electron microscopy (SEM) and the mean field homogenization approach. This makes it possible to use an electron microscope to image fiber samples corresponding to the sample size using microtomography. Molded samples and pellets of four polyamide-6 short-glass fiber-reinforced composites with mass fractions of 15%, 30%, and 50% were considered. The aspect ratio distribution measured by SEM for the investigated materials was 20.25 with a coefficient of variation of 5.1%. The fiber aspect ratio obtained based on mean field homogenization theory and the tensile curve approximation was underestimated at 13.698 with a coefficient of variation of 5.2%. The deviation between the micro- and macro-estimates can be represented as a mean effective aspect ratio of 68% with a coefficient of variation of 8.5%. The developed technology for preparing samples for SEM and automated image processing can be used to study other short-reinforced polymer composite materials. The obtained estimates can serve as a useful reference when calibrating other models of short-fiber-reinforced polymer materials. [ABSTRACT FROM AUTHOR]

This paper discusses an approach to estimating the parameters of the cohesive zone model (CZM) by mode II by extruding the bushing along the lug axis. This method of evaluation requires small samples, which is particularly relevant when investigating short fiber-reinforced polymers (SFRPs) with additively manufactured embedded elements. Adhesion is investigated on the example of 30% carbon fiber-reinforced polyamide-6 molded to Ti-6Al-4V (VT6) selective laser-melted (SLM) alloy bushing in cases of a roughness Ra = 2.66 μm (vibratory finishing), Ra = 8.79 μm (sandblasting), and Ra = 10.02 (directly from SLM). The values of the maximum equivalent tangential contact stress were in a range from 1.1 MPa to 9.5 MPa, while the critical fracture energy for tangential slip was estimated at 15 N/mm for all cases. Experimental validation of the obtained CZM mode II was carried out by evaluating the load-carrying capacity of the lugs with different bushings. In both the experiment and the calculation, greater bushing roughness provides greater lug load-bearing capacity. The ribbed bushings added significant strength in the experiments, which confirmed the importance of considering the tangential mode in the contact model. The presented models can be used for the preliminary evaluation of short fiber-reinforced polyamide-6 parts with titanium-embedded elements bearing capacity. [ABSTRACT FROM AUTHOR]

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Estimating climate response to observed and projected increases in atmospheric greenhouse gases usually requires averaging among multiple independent simulations of computationally expensive global climate models to filter out the internal climate variability. Studies have shown that advanced pattern recognition methods allow one to obtain accurate estimates of the forced climate signal from just a handful of such climate realizations. The accuracy of these methods for a fixed ensemble size, however, decreases with an increasing magnitude of the low-frequency, decadal and longer internal climate variability. Here we generalize a previously developed Bayesian methodology of Linear Dynamical Mode (LDM) decomposition for spatially extended time series to enable joint identification and analysis of forced signal and internal variability in ensembles of climate simulations, a methodology dubbed here an ensemble LDM, or ELDM. The new ELDM method is shown to outperform its pattern-recognition competitors by more accurately isolating the forced signal in small ensembles of both toy- and state-of-the-art climate-model simulations. It is able to do so by explicitly recognizing a non-random structure of the internal variability, identified by the ELDM algorithm alongside the optimal forced-signal estimate, which allows one to study possible dynamical connections between the two types of variability. The optimal ELDM filtering provides a unique opportunity for objective intercomparison of decadal and longer climate variability across different global climate models—a task that proved difficult due to uncertainties associated with the noisy character and limited length of historical climate simulations combined with parameter uncertainties of alternative signal-detection methods. [ABSTRACT FROM AUTHOR]

A fully developed turbulent flow in a concentric annulus, Re = 10,000 , r i / r o = 0.5 , with an inner rotating cylinder in the velocity range N = U ω / U b = 0 ÷ 4 , is studied via a large-eddy simulation. Also, for comparison, simulations by steady-state, unstatiounary RANS k- ω SST (URANS), and Elliptic Blending Model (EBM) were made. The main focus of this study is on the effect of high rotation on the mean flow, turbulence statistics, and vortex structure. Distribution of the tangential velocity and the Reynolds stress tensor change their behaviour at N > 0.5 ∼ 1 . With rotation increases, the production of tangential fluctuation becomes dominant over axial ones and the position of turbulent kinetic energy maximum shifts towards the wall into the buffer zone. URANS and EBM approaches show good agreement with LES in mean flow, turbulent statistics, and integral parameters. The difference in pressure loss prediction between LES and URANS does not exceed 20%, but the average difference is about 11%. The EBM approach underestimates pressure losses up to 9% and on average not more than 5%. Vortex structures are described well by URANS. [ABSTRACT FROM AUTHOR]

It is well-known that the upper ocean heat content (OHC) variability in the tropical Pacific contains valuable information about dynamics of El Niño–Southern Oscillation (ENSO). Here we combine sea surface temperature (SST) and OHC indices derived from the gridded datasets to construct a phase space for data-driven ENSO models. Using a Bayesian optimization method, we construct linear as well as nonlinear models for these indices. We find that the joint SST-OHC optimal models yield significant benefits in predicting both the SST and OHC as compared with the separate SST or OHC models. It is shown that these models substantially reduce seasonal predictability barriers in each variable—the spring barrier in the SST index and the winter barrier in the OHC index. We also reveal the significant nonlinear relationships between the ENSO variables manifesting on interannual scales, which opens prospects for improving yearly ENSO forecasting. [ABSTRACT FROM AUTHOR]

Natural and social systems exhibit complex behavior reflecting their rich dynamics, whose governing laws are not fully known. This study develops a unified data-driven approach to estimate predictability of such systems when several independent realizations of the system's evolution are available. If the underlying dynamics are quasi-linear, the signal associated with the variable external factors, or forcings, can be estimated as the ensemble mean; this estimation can be optimized by filtering out the part of the variability with a low ensemble-mean-signal-to-residual-noise ratio. The dynamics of the residual internal variability is then encapsulated in an optimal, in a Bayesian sense, linear stochastic model able to predict the observed behavior. This model's self-forecast covariance matrices define a basis of patterns (directions) associated with the maximum forecast skill. Projecting the observed evolution onto these patterns produces the corresponding component time series. These ideas are illustrated by applying the proposed analysis technique to (1) ensemble data of regional sea-surface temperature evolution in the tropical Pacific generated by a state-of-the-art climate model and (2) consumer-spending records across multiple regions of the Russian Federation. These examples map out a range of possible solutions—from a solution characterized by a low-dimensional forced signal and a rich spectrum of predictable internal modes (1)—to the one in which the forced signal is extremely complex, but the number of predictable internal modes is limited (2). In each case, the proposed decompositions offer clues into the underlying dynamical processes, underscoring the usefulness of the proposed framework. [ABSTRACT FROM AUTHOR]

Worldwide populations of shorebirds are declining, associated with a complex interplay of climate change, predation, human disturbance and habitat degradation. Comprehensive information on the distribution and breeding ecology of shorebird populations is crucial to understand and mitigate these threats. Kazakhstan, the largest country in Central Asia, comprises multiple flyways and breeding habitats for shorebird species, including the Kentish Plover Charadrius alexandrinus, but information on the population size and breeding distribution of shorebird species in the region is highly limited. We conducted a wide‐scale survey of Kentish Plover across Kazakhstan during the breeding season and utilize species distribution modelling to outline key anthropogenic and environmental variables that determine Kentish Plover presence. Our results reveal widespread distribution of Kentish Plovers across Kazakhstan but indicate that breeding densities are generally low. Our distribution modelling stresses the primary importance of proximity to water bodies and climate as the main predictors of Kentish Plover presence, but reveals a weak association with indicators of human disturbance. We utilize our distribution modelling to provide the first quantitative estimate of the breeding population size of Kentish Plover in Kazakhstan, which indicates a modest number of individuals given the size of the country (between 12 000 and 32 000 individuals). Our results indicate the key routes via which climate change may impact on population‐level distributions of Kentish Plover and provide a platform for future studies investigating species distributions across similarly vast and inaccessible regions. [ABSTRACT FROM AUTHOR]

The ship's resistance and manoeuvrability in shallow waters can be adversely influenced by the presence of fluid mud layers on the seabed of ports and waterways. Fluid mud exhibits a complex non-Newtonian rheology that is often described using the Herschel–Bulkley model. The latter has been recently implemented in a maritime finite-volume CFD code to study the manoeuvrability of ships in the presence of muddy seabeds. In this paper, we explore the accuracy and robustness of the CFD code in simulating the flow of Herschel–Bulkley fluids, including power-law, Bingham and Newtonian fluids as particular cases. As a stepping stone towards the final maritime applications, the study is carried out on a classic benchmark problem in non-Newtonian fluid mechanics: the laminar flow around a sphere. The aim is to test the performance of the non-Newtonian solver before applying it to the more complex scenarios. Present results could also be used as reference data for future testing. Flow simulations are carried out at low Reynolds numbers in order to compare our results with an extensive collection of data from the literature. Results agree both qualitatively and quantitatively with literature. Difficulties in the convergence of the iterative solver emerged when simulating Bingham and Herschel–Bulkley flows. A simple change in the interpolation of the apparent viscosity has mitigated such difficulties. The results of this work, combined with our previous code verification exercises, suggest that the non-Newtonian solver works as intended and it can be thus employed on more complex applications. [ABSTRACT FROM AUTHOR]