با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
A DIC method to determine the Mode I energy release rate G, the Jintegral and the traction-separation law simultaneously for adhesive joints
یک روش DIC برای تعیین میزان آزاد سازی انرژی حالت من G ، Jintegral و قانون جداسازی کشش به طور همزمان برای اتصالات چسبنده-2020
The quasi-static Mode I fracture behaviour of joints bonded with either a brittle or toughened epoxy adhesive or a ductile polyurethane adhesive has been investigated by means of digital image correlation (DIC). A novel method to measure the crack length using DIC analysis is proposed. By measuring the crack tip separation, beam rotation and crack length, the energy release rate G and the J-integral are obtained and are compared to analyse the validity of Linear Elastic Fracture Mechanics (LEFM) methods. Simultaneously the traction-separation laws (TSLs) for the adhesive joints were measured. The TSLs were then used as input data for FE modelling to evaluate their accuracy by comparing with experimental results. It is shown that LEFM is valid for the joints bonded with either the brittle or toughened epoxy adhesives but is invalid for joints bonded with the polyurethane adhesive. The procedure proposed here to measure the crack length via DIC shows great promise and can be automated readily in practice.
Keywords: Traction-separation law (TSL) | G and J-integral | Crack length measurement | Validity of LEFM methods | Digital image correlation | Adhesive joint
Wake modeling of wind turbines using machine learning
مدل سازی توربین های بادی با استفاده از یادگیری ماشین-2020
In the paper, a novel framework that employs the machine learning and CFD (computational fluid dynamics) simulation to develop new wake velocity and turbulence models with high accuracy and good efficiency is proposed to improve the turbine wake predictions. An ANN (artificial neural network) model based on the backpropagation (BP) algorithm is designed to build the underlying spatial relationship between the inflow conditions and the three-dimensional wake flows. To save the computational cost, a reduced-order turbine model ADM-R (actuator disk model with rotation), is incorporated into RANS (Reynolds-averaged Navier-Stokes equations) simulations coupled with a modified k − ε turbulence model to provide big datasets of wake flow for training, testing, and validation of the ANN model. The numerical framework of RANS/ADM-R simulations is validated by a standalone Vestas V80 2MW wind turbine and NTNU wind tunnel test of double aligned turbines. In the ANN-based wake model, the inflow wind speed and turbulence intensity at hub height are selected as input variables, while the spatial velocity deficit and added turbulence kinetic energy (TKE) in wake field are taken as output variables. The ANN-based wake model is first deployed to a standalone turbine, and then the spatial wake characteristics and power generation of an aligned 8-turbine row as representation of Horns Rev wind farm are also validated against Large Eddy Simulations (LES) and field measurement. The results of ANNbased wake model show good agreement with the numerical simulations and measurement data, indicating that the ANN is capable of establishing the complex spatial relationship between inflow conditions and the wake flows. The machine learning techniques can remarkably improve the accuracy and efficiency of wake predictions.
Keywords: Wind turbine wake | Wake model | Artificial neural network (ANN) | Machine learning | ADM-R (actuator-disk model with rotation) | model | Computational fluid dynamics (CFD)
CFD data based neural network functions for predicting hydrodynamic performance of a low-pitch marine cycloidal propeller
توابع شبکه عصبی مبتنی بر داده های CFD برای پیش بینی عملکرد هیدرودینامیکی یک پروانه سیکلوئید دریایی کم فشار-2020
Today, various types of propulsion systems are used in different purpose ship types. Marine cycloidal propeller (MCP) is one of these propulsion systems, which has been designed for ships that require high maneuverability. MCP can be considered as an especial type of marine propulsion systems, since it produces the thrust force which is perpendicular to propeller axis of rotation. The magnitude and direction of the thrust force can be adjusted by controlling the pitching angle of the blades, so no separate rudder is needed to manoeuvre the ship. In this study, mathematical functions for predicting the open water hydrodynamic performance of a low-pitch MCP are presented by training a neural network based on computational fluid dynamics (CFD) data. For this purpose, the four nondimensional parameters of blade number (Z), ratio of blade thickness to MCP diameter (t/D), pitch (e) and advance coefficient (λ) are considered as input variables, whereas the hydrodynamic coefficients of thrust (Ks) and torque (Kd) are considered as targets. CFD simulations are performed for different cases of MCP with different combinations of Z, t/D, e and λ. The results showed that a two-layer feedforward network with one hidden layer of sigmoid neurons and at least 4 neurons in the hidden layer can be well trained by CFD data in order to obtain functions with good accuracy in predicting Ks and Kd coefficients of a low-pitch MCP.
Keywords: Marine cycloidal propeller | Hydrodynamic performance | CFD simulation | Neural network | Predictive function
Development of mature second-growth Sequoia sempervirens forests
Development of mature second-growth Sequoia sempervirens forests-2020
Mature second-growth coast redwood (Sequoia sempervirens) forests—logged over 100 yr ago—are an important resource in the redwood region, but development of regenerating forests beyond rotation age (~50 yr) is not well understood. Continuous long-term data are especially lacking, considering that the maximum possible age of second-growth stands is over 160 yr. Here we examine accumulation of tree biomass, leaf area, and canopy structure in three mature second-growth forests in California (Arcata, Big River, Oakland) that range in age from 133 to 159 yr since clearcut logging. Four fixed-area plots form the basis of this examination, including three 1- acre plots established in 1923 that together permit examination of Sequoia forest development over nearly a century. The four plots held 963 to 1476 Mg ha−1 of aboveground tree biomass and 9.0 to 13.7 of tree leaf area index. From 1923 to 2017, the Big River plot exhibited rapid biomass accumulation (up to 23 Mg ha−1 yr−1) as a densely stocked nearly pure Sequoia stand on an alluvial terrace. The two low-elevation plots near Arcata exhibited slower (up to 13 Mg ha−1 yr−1) but consistent growth with an increasing dominance of Sequoia, as cooccurring conifers (Abies grandis, Picea sitchensis, Pseudotsuga menziesii) steadily declined in number despite rapid early growth. Increasing Sequoia dominance, substantial density-independent mortality in recent decades, and shifts in tree size distributions illustrate structural maturation as these forests approach an old-growth condition. The Oakland plot, which occurred ~ 300 m higher in elevation and received less than two-thirds as much annual rainfall, was a nearly pure Sequoia stand with canopy structure similar to, but shorter than, the Big River plot. Despite differences in elevation and rainfall, aboveground biomass of individual large trees in all four plots exceeded 15 Mg with Sequoia biomass increments averaging > 200 kg yr−1 during the 21st century. Characteristics and developmental trajectories of these plots provide realistic benchmarks for management of alluvial, upland, and inland Sequoia forests.
Keywords: Sequoia sempervirens | Second-growth forest | Fixed-area plot | Biomass | Leaf area | Structural development
CFD analysis of second law characteristics for flow of a hybrid biological nanofluid under rotary motion of a twisted tape: Exergy destruction and entropy generation analyses
تجزیه و تحلیل CFD از ویژگی های قانون دوم برای جریان یک نانوسیالات بیولوژیکی ترکیبی تحت حرکت چرخشی نوار پیچ خورده: تخریب اگزرژی و تجزیه و تحلیل نسل آنتروپی-2020
The main goal of the current study is to evaluate the effects of a hybrid heat transfer enhancement method, i.e. simultaneous deployment of active and passive techniques, with employing an ecofriendly-produced graphene nanoplatelets nanofluid from the viewpoint of the second law of thermodynamics. The numerical simulations are performed for the tubes enhanced with the innovative rotary twisted tape. Employing the rotary twisted tape intensifies the flow mixing, disrupts the thermal boundary layer, and reduces the temperature gradients dramatically. The total entropy generation and total exergy destruction of nanofluid extremely diminish by the rotational speed elevation. The maximum decrement in the total entropy generation is 87.38%, which occurs by elevating the rotational speed from 0 to 900 rpm. The concentration increase considerably contributes to the exergy destruction decrement besides total entropy generation reduction. The lower twisted ratio exhibits the smaller thermal irreversibility, and the best second law efficiency equals to 0.932.
Keywords: Exergy destruction | Rotational twisted tape | Graphene nanoplatelets nanofluid | Entropy generation | Ecofriendly nanofluid | Second law efficiency
Identification of animal individuals using deep learning: A case study of giant panda
شناسایی فردی حیوانی با استفاده از یادگیری عمیق: یک مطالعه موردی از پاندا غول پیکر-2020
Giant panda (Ailuropoda melanoleuca) is an iconic species of conservation. However, long-term monitoring of wild giant pandas has been a challenge, largely due to the lack of appropriate method for the identification of target panda individuals. Although there are some traditional methods, such as distance-bamboo stem fragments methods, molecular biological method, and manual visual identification, they all have some limitations that can restrict their application. Therefore, it is urgent to explore a reliable and efficient approach to identify giant panda individuals. Here, we applied the deep learning technology and developed a novel face-identification model based on convolutional neural network to identify giant panda individuals. The model was able to identify 95% of giant panda individuals in the validation dataset. In all simulated field situations where the quality of photo data was degraded, the model still accurately identified more than 90% of panda individuals. The identification accuracy of our model is robust to brightness, small rotation, and cleanness of photos, although large rotation angle (> 20°) of photos has significant influence on the identification accuracy of the model (P < 0.01). Our model can be applied in future studies of giant panda such as long-term monitoring, big data analysis for behavior and be adapted for individual identification of other wildlife species.
Keywords: Deep learning | convolutional neural network | Individual identification | Giant panda
Deep learning for continuous manufacturing of pharmaceutical solid dosage form
یادگیری عمیق برای تولید مداوم فرم دوز جامد دارویی-2020
Continuous Manufacturing (CM) of pharmaceutical drug products is a new approach within the pharmaceutical industry. In the presented paper, a GMP continuous wet granulation line for production of solid dosage forms was investigated. The line was composed of the subsequent continuous unit: operations feeding – twin-screw wet-granulation – fluid-bed drying – sieving and tableting. The formulation of a commercial entity was selected for this study. Several critical process parameters were evaluated in order to probe the process and to characterize the impact on quality attributes. Seven critical process parameters have been selected after a risk analysis: API and excipient mass flows of the two feeders, liquid feed rate and rotation speed of the extruder and rotation speed, temperature and airflow of the dryer. Eight quality attributes were controlled in real time by Process Analytical Technologies (PAT): API content after blender, after dryer, in tablet press feed frame and of tablet, LOD after dryer and PSD after dryer (three PSD parameters: x10 x50 x90). The process parameter values were changed during production in order to detect the impact on the quality of the final product. The deep learning techniques have been used in order to predict the quality attribute (output) with the process parameters (input). The use of deep learning reduces the noise and simplify the data interpretation for a better process understanding. After optimization, three hidden layers neural network were selected with 6 hidden neurons. The activation function ReLU (Rectified Linear Unit) and the ADAM optimizer were used with 2500 epochs (number of learning cycle). API contents, PSD values and LOD values were estimated with an error of calibration lower than 10%. The level of error allow an adequate process monitoring by DNN and we have proven that the main critical process parameters can be identified at a higher levelof process understanding. The synergy between PAT and process data science creates a superior monitoring framework of the continuous manufacturing line and increase the knowledge of this innovative production line and the products that it makes.
Keywords: Continuous manufacturing | Solid dosage form | Process monitoring | Process analytical technology | Deep learning | Process data science | Process data analytics
Quantum recurrent encoder–decoder neural network for performance trend prediction of rotating machinery
شبکه عصبی رمزگذار- رمزگذار مکرر کوانتومی برای پیش بینی روند عملکرد ماشین های چرخشی-2020
Traditional neural networks generally neglect the primary and secondary relationships of input information and process the information indiscriminately, which leads to their bad nonlinear approximation capacity and low generalization ability. As a result, traditional neural networks always show poor prediction accuracy in the performance degradation trend prediction of rotating machinery (RM). In view of this, a novel neural network called quantum recurrent encoder–decoder neural network (QREDNN) is proposed in this paper. In QREDNN, the attention mechanism is used to simultaneously reconstruct encoder and decoder of QREDNN, so that QREDNN can fully excavate and pay attention to important information but suppress the interference of redundant information to obtain better nonlinear approximation capacity. On the other hand, the quantum neuron is used to construct a new quantum gated recurrent unit (QGRU) in which activation values and weights are represented by quantum rotation matrices. The QGRU can traverse the solution space more finely and has a lot of multiple attractors, so it can replace the traditional recurrent unit of the encoder and decoder and enhance the generalization ability and response speed of QREDNN. Moreover, the Levenberg– Marquardt (LM) algorithm is introduced to improve the update speeds of the rotation angles of quantum rotation matrices and the attention parameters of QREDNN. Based on the superiorities of QREDNN, a new performance trend prediction method for RM is proposed, in which the denoised fuzzy entropy (DFE) of vibration acceleration signal of RM is input into QREDNN as the performance degradation feature for predicting the performance degradation trend of RM. The examples of predicting the performance trend of rolling bearings demonstrate the effectiveness of our proposed method.
Keywords: Quantum recurrent encoder–decoder | neural network (QREDNN) | Artificial intelligence | Attention mechanism | Quantum neuron | Performance trend prediction | Rotating machinery
Influence of the inlet distortion on fan stall margin at different rotational speeds
تاثیر شکل ورودی در حاشیه فن در سرعت های مختلف چرخشی-2020
The aim of this paper is to develop a reliable and accurate numerical strategy that can be used to study the effects of inlet distortions on the aerodynamic stability of fan blades in aero-engines. As an initial step towards achieving this goal, three-dimensional unsteady Reynolds-Averaged-Navier-Stokes (URANS) simulations were carried out to predict the influence of total pressure distortion on the loss of stall margin of a fan blade. NASA rotor 67, for which a significant amount of measured steady and unsteady data is available, was used for this study. It was observed that the size of the time step has a significant effect on the solution near stall and hence the stall margin of the blade. In the second part of this work, unsteady simulations were carried out to study the effects of rotational speed on the stall margin and stalled operating point of the blade. The results showed that for the same level and pattern of inlet distortion, the stall margin of the blade decreases as the corrected speed decreases. However, the in-stall total pressure losses decrease as the speed decreases. Finally, in our research it has become apparent that there is a big lack of public domain measured data for the cases with inlet distortion, and therefore, validated CFD results can be very helpful to other researchers in the field.
The forgotten land use class: Mapping of fallow fields across the Sahel using Sentinel-2
کلاس استفاده از اراضی فراموش شده: نقشه برداری از مزارع مزرعه در ساحل با استفاده از سنتینل-2-2020
Remote sensing-derived cropland products have depicted the location and extent of agricultural lands with an ever increasing accuracy. However, limited attention has been devoted to distinguishing between actively cropped fields and fallowed fields within agricultural lands, and in particular so in grass fallow systems of semiarid areas. In the Sahel, one of the largest dryland regions worldwide, crop-fallow rotation practices are widely used for soil fertility regeneration. Yet, little is known about the extent of fallow fields since fallow is not explicitly differentiated within the cropland class in any existing remote sensing-based land use/cover maps, regardless of the spatial scale. With a 10 m spatial resolution and a 5-day revisit frequency, Sentinel-2 satellite imagery made it possible to disentangle agricultural land into cropped and fallow fields, facilitated by Google Earth Engine (GEE) for big data handling. Here we produce the first Sahelian fallow field map at a 10 m resolution for the baseline year 2017, accomplished by designing a remote sensing driven protocol for generating reference data for mapping over large areas. Based on the 2015 Copernicus Dynamic Land Cover map at 100 m resolution, the extent of fallow fields in the cropland class is estimated to be 63% (403,617 km2) for the Sahel in 2017. Similar results are obtained for five contemporary cropland products, with fallow fields occupying 57–62% of the cropland area. Yet, it is noted that the total estimated area coverage depends on the quality of the different cropland products. The share of cropped fields within the Copernicus cropland area is found to be higher in the arid regions (200–300 mm rainfall) as compared to the semi-arid regions (300–600 mm rainfall). The woody cover fraction within cropped and fallow fields is found to have a reversed pattern between arid (higher woody cover in cropped fields) and semi-arid (higher woody cover in fallow fields) regions. The method developed, using cloud-based Earth Observation (EO) data and computation on the GEE platform, is expected to be reproducible for mapping the extent of fallow fields across global croplands. Future applications based on multi-year time series is expected to improve our understanding of crop-fallow rotation dynamics in grass fallow systems being key in teasing apart how cropland intensification and expansion affect environmental variables, such as soil fertility, crop yields and local livelihoods in low-income regions such as the Sahel. The mapping result can be visualized via a web viewer (https://buwuyou.users.earthengine.app/view/fallowinsahel).
Keywords: Fallow fields | Cropland | Satellite image time series | Land use/cover mapping | Sentinel-2 | Drylands | Sahel