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1 |
Understanding the effect of surfactants on two-phase flow using computer vision
درک اثر سورفکتانت ها بر جریان دو فازی با استفاده از بینایی کامپیوتر-2022 The effect of surfactants on vertical gas-liquid flow is experimentally investigated in a 12.7 mm diameter
tube at conditions relevant to an ammonia-water bubble absorber. The characteristics of two-phase flow
are studied using an air-water mixture, both with and without the addition of 1-octanol as the surfac-
tant. High-speed videography is used to study the flow patterns and quantify interfacial areas and bubble
velocities. Novel computer vision-based methods are used to analyze and quantify these flow parame-
ters. The addition of 1-octanol results in enhancement in interfacial area due to the prevention of bubble
coalescence leading to many small diameter bubbles. Measured values of interfacial area are compared
with predictions from correlations in the literature, and agreement and differences are interpreted and
discussed. The bubble velocity is measured by object tracking using the optical flow method. Surfactants
lead to a decrease in bubble velocity and increase in the residence time. These are surmised to be due
to the shear stresses caused by the non-uniform concentration distribution of surfactant along the bub-
ble surface. Overall, the addition of surfactants can lead to appreciable enhancement in heat and mass
transfer rates due to their effect on interfacial areas and residence times. keywords: سورفکتانت ها | جریان دو فازی | ناحیه رابط | سرعت | تقویت | تجسم جریان | Surfactants | Two-phase flow | Interfacial area | Velocity | Enhancement | Flow visualization |
مقاله انگلیسی |
2 |
Spatiotemporal flow features in gravity currents using computer vision methods
ویژگی های جریان مکانی-زمانی در جریان های گرانشی با استفاده از روش های بینایی کامپیوتری-2022 Relationships between the features visually identified at the front of the flow’s current and parameters
regarding its velocity and turbulence were observed in early experimental works on the characterization of
gravity currents. Researches have associated front features, like lobes and clefts, with the flow’s turbulence, and
have used these associations ever since. In more recent works using numerical simulations, these connections
were still being validated for various flow parameters at higher front velocities. The majority of works regarding
measurements at the front of a gravity current rely on the front’s images for making its analysis and establish
relationships. Besides that, there is an interdisciplinary field related to computer science called computer vision,
devoted to study how digital images can be analyzed and how these results can be automated. This paper
describes the use of computer vision algorithms, particularly corner detection and optical flow, to automatically
track features at the front of gravity currents, either from physical or numerical experiments. To determine the
proposed approach’s accuracy, we establish a ground-truth method and apply it to numerical simulation results
data sets. The technique used to trace the front features along the flow showed promising results, especially
with higher Reynolds numbers flows.
keywords: جریان های گرانشی | ساختارهای لوب و شکاف | روش های کامپیوتری | ویژگی ردیابی | Gravitycurrents | Lobesandcleftsstructures | Computervisionmethods | Featurepointtracking |
مقاله انگلیسی |
3 |
Refraction seismic complementing electrical method in subsurface characterization for tunneling in soft pyroclastic, (a case study)
روش الکتریکی تکمیلی لرزهای شکست در شناسایی زیرسطحی برای تونلزنی در آذرآواری نرم (مطالعه موردی)-2021 The paper highlights the potential drawback of mapping a single geophysical property for subsurface characterization in potential engineering sites. As an exemplary case study, we present the geophysical survey conducted
along the surface projection of a tunnel in the quaternary volcanic terrain of the Main Ethiopia Rift. Initially,
geoelectrical mapping involving 12 Vertical Electrical Sounding (VES) and a short Electrical Resistivity Imaging
(ERI) line, was carried out. The 1D geoelectric model indicates that the formation resistivity at tunnel zone varies
from 50 to 500 Ω∙m. The corresponding value on 2D model, (>350 Ω∙m), is also compatible. Based on limited
available geological information, the geoelectric horizon was attributed to weathered and variably saturated
ignimbrite. Following unexpected encounter during excavation, refraction seismic and core drilling were carried
out for additional insights. Tomographic analysis of the seismic arrival times revealed that below a depth of 45 m,
(tunnel zone), the velocity substratum is marked by a range, (1200–1800 m/s). Such low velocity range is typical
of unconsolidated materials and, thus, cannot rationalize the geoelectrical attribution (ignimbrite). In a joint
interpretation, the likely formation that may justify the observed range of the electrical resistivity and low P-wave
velocity appears to be unwelded pyroclastic deposit (volcanic ash). Eventually, core samples from the tunnel zone
confirmed the presence of thick ash flow. However, the unexpected ground conditions encountered at the early
phase, due to insufficient information derived from a single geophysical parameter, caused extra cost and
considerable delay.
Keywords: Integrated approach | Refraction seismic | DC resistivity | Subsurface characterization | Main Ethiopian Rift (MER) |
مقاله انگلیسی |
4 |
Significance of variable electrical conductivity on non-Newtonian fluid flow between two vertical plates in the coexistence of Arrhenius energy and exothermic chemical reaction
Noneاهمیت رسانایی الکتریکی متغیر بر جریان سیال غیرنیوتنی بین دو صفحه عمودی در همزیستی انرژی آرنیوس و واکنش شیمیایی گرمازا-2021 The present study is designed to model the combustible materials of two vertical plates with Arrhenius energy
and exothermic chemical reaction. The magnetohydrodynamics fluid is considered to experience an exothermic
chemical reaction inside the channel. Additional effects incorporated to the novelty of the model are the
rheological Casson fluid term and the variable electrical conductivity. The model has transformed appropriately
to its dimensionless form using similarity renovation and the Solution is numerically obtained using the
Chebyshev collocation scheme. The influences of controlling parameters on the fluid velocity, temperature,
concentration, and heat transfer rate are analyzed using graph and quantitatively discussed. Analyses reveal
that the activation energy declines the fluid velocity, while the existence of the variable electrical conductivity
parameter has the opposite effect. The heat transfer rate is enhanced with higher values of concentration
buoyancy (Gc) and variable electrical conductivity parameter. Moreover, the non-Newtonian Casson fluid
parameter shows a solid characteristic when yield stress is more than the shear stress. Thermal and chemical
engineering, as well as service-worthiness of industrial products, will benefit from the findings of this study.
Keywords: Magnetohydrodynamics fluid | Rheological Casson fluid | Arrhenius energy and exothermic chemical | reaction |
مقاله انگلیسی |
5 |
Blurry vision: Supply chain visibility for personal protective equipment during COVID-19
تاری دید: دید زنجیره تامین برای تجهیزات محافظتی شخصی در طول COVID-19-2021 We explore supply chain visibility challenges in the context of our contemporary COVID pandemic, and offer
insights, models and potential solutions to remove barriers to clear supply chain visibility. In this paper, we
describe how visibility and velocity are the two key attributes that are required to enabling critical decision making accuracy which will in turn increase the ability of local, state and federal healthcare and public
health decision-makers to response to shifts in the U.S. system. We describe the problems in current systems due
to the lack of visibility of material in global supply chains, which in turn leads to problems such as the lack of PPE
that occurred during the COVID pandemic. We conclude with recommendations on how to render inventory
more visible for the future.
Keywords: COVID | Public purchasing | Inventory | Visibility | Global sourcing |
مقاله انگلیسی |
6 |
یک مدل برای شبیهسازی و طرحریزی پویای مسیر تعویض باند مبتنی بر تابع پارامتری جدید
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 16 - تعداد صفحات فایل doc فارسی: 22 مسألهی تعویض باند (LC) میتواند موجب تصادفات شدید شده و ترافیک آزاردهندهای را در جادههای چندبانده ایجاد نماید. مدل موجود برای شبیهسازی LC با یک سری محدودیتها (انطباق کم، فقدان مشخصههای سرعت و شتاب، انحنای زیاد) با استفاده از منحنی مسیرهای شناختهشدهای همچون منحنی مماس هایپربولیک (HTC)، منحنی مبتنی بر سینوس (SC)، و منحنی چندجملهای (PC) ایجاد شد. در این مقاله، یک منحنی پارامتری جدید با استفاده از دستگاه مختصات خمیدهخطی ارائه و با پایگاه دادهی واقعی شبیهسازی نسل آتی (NGSIM) انطباق داده شد. سپس مشخصههای جدید سرعت و شتاب با استفاده از منحنی مسیر LC پیشنهاد شدند. انحنای مدل پیشنهادی در هر دو نقطهی آغاز و پایان LC، انحنای مبتنی بر صفر بود. این انحنای پیشنهادی با دو مدل همانند HTC و SC مقایسه شد. خطای متوسط جذر میانگین مربعات مدل پیشنهادی در مقایسه با مدل HTC، برای LC چپ به میزان 1.84% و برای LC راست به میزان 15.48% و در مقایسه با مدل SC به میزان 1.74% برای LC چپ و به میزان 15.60% برای LC راست کاهش مییابد. بطور مشابه، مدل پیشنهادی برای مشخصههای سرعت و شتاب نسبت به مدل PC تا حد زیادی بهبود مییابد. منحنی پارامتری پیشنهادی، نقاط فاصله و برخورد خودروی LC با یک خودروی جلویی و خودروی پشتی در باند هدف را حل میکند و میتوان از آن در برنامهریزی مسیر LC واقعی استفاده کرد.
کلیدواژه ها: مشخصههای شتاب | منحنی پارامتری | سرعت | برنامهریزی مسیر |
مقاله ترجمه شده |
7 |
Improving supply chain resilience through industry 4:0: A systematic literature review under the impressions of the COVID-19 pandemic
بهبود انعطاف پذیری زنجیره تأمین از طریق صنعت 4:0: بررسی ادبیات سیستماتیک تحت تأثیر همه گیری COVID-19-2021 The COVID-19 pandemic is one of the most severe supply chain disruptions in history and has challenged practitioners and scholars to improve the resilience of supply chains. Recent technological progress, especially industry 4.0, indicates promising possibilities to mitigate supply chain risks such as the COVID-19 pandemic. However, the literature lacks a comprehensive analysis of the link between industry 4.0 and supply chain resilience. To close this research gap, we present evidence from a systematic literature review, including 62 papers from high-quality journals. Based on a categorization of industry 4.0 enabler technologies and supply chain resilience antecedents, we introduce a holistic framework depicting the relationship between both areas while exploring the current state-of-the-art. To verify industry 4.0’s resilience opportunities in a severe supply chain disruption, we apply our framework to a use case, the COVID-19-affected automotive industry. Overall, our results reveal that big data analytics is particularly suitable for improving supply chain resilience, while other industry 4.0 enabler technologies, including additive manufacturing and cyber-physical systems, still lack proof of effectiveness. Moreover, we demonstrate that visibility and velocity are the resilience antecedents that benefit most from industry 4.0 implementation. We also establish that industry 4.0 holistically supports pre-disruption resilience measures, enabling more effective proactive risk management. Both research and practice can benefit from this study. While scholars may analyze resilience potentials of under-explored enabler technologies, practitioners can use our findings to guide industry 4.0 investment decisions. Keywords: Industry 4.0 | Supply chain risk management | Supply chain resilience | Supply chain disruption | Digital supply chain | Literature review |
مقاله انگلیسی |
8 |
Prediction of perforation into concrete accounting for saturation ratio influence at high confinement
پیش بینی سوراخ شدن در بتن برای تأثیر نسبت اشباع در محصور شدن بالا-2021 This paper provides both an analytical and a finite element models aiming at better predicting possible perfo-
ration of reinforced concrete slabs submitted to impacts. Both models account for free water saturation ratio and
high triaxial stress induced into concrete by the impact. Finite element simulations are performed with Abaqus
explicit code using a revised constitutive model for concrete; this coupled damage plasticity model (PRM) ac-
counts for strain rate effects and the influence of saturation ratio on the triaxial behavior. Complementary
original analytical predictions of ballistic limit and residual velocities are provided for both hard and soft im-
pacts. These predictions depend on a recent deviatoric stress-based formulation of compressive strength of
concrete. Numerical and analytical results are consistent with bending and punching experimental tests. keywords: اثرات نرم و سخت | سرعت باقی مانده | بتن آرمه | ظرفیت سوراخ کردن | نسبت اشباع | Soft and hard impacts | Residual velocity | Reinforced concrete | Perforation capacity | Saturation ratio |
مقاله انگلیسی |
9 |
Automated Vision-Based Microsurgical Skill Analysis in Neurosurgery Using Deep Learning: Development and Preclinical Validation
تجزیه و تحلیل خودکار مهارتهای میکروجراحی مبتنی بر بینایی در جراحی مغز و اعصاب با استفاده از یادگیری عمیق: توسعه و اعتبار پیش بالینی-2021 - BACKGROUND/OBJECTIVE: Technical skill acquisition
is an essential component of neurosurgical training.
Educational theory suggests that optimal learning and
improvement in performance depends on the provision of
objective feedback. Therefore, the aim of this study was to
develop a vision-based framework based on a novel representation of surgical tool motion and interactions
capable of automated and objective assessment of microsurgical skill.
- METHODS: Videos were obtained from 1 expert, 6 intermediate, and 12 novice surgeons performing arachnoid dissection in a validated clinical model using a standard operating microscope. A mask region convolutional neural network framework was used to segment the tools present within the operative field in a recorded video frame. Tool motion analysis was achieved using novel triangulation metrics. Performance of the framework in classifying skill levels was evaluated using the area under the curve and accuracy. Objective measures of classifying the surgeons skill level were also compared using the ManneWhitney U test, and a value of P < 0.05 was considered statistically significant. - RESULTS: The area under the curve was 0.977 and the accuracy was 84.21%. A number of differences were found, which included experts having a lower median dissector velocity (P [ 0.0004; 190.38 mse1 vs. 116.38 mse1), and a smaller inter-tool tip distance (median 46.78 vs. 75.92; P [ 0.0002) compared with novices. - CONCLUSIONS: Automated and objective analysis of microsurgery is feasible using a mask region convolutional neural network, and a novel tool motion and interaction representation. This may support technical skills training and assessment in neurosurgery. Key words: Artificial intelligence | Computer vision | Convolutional neural network | Mask RCNN | Microsurgery | Motion-analysis | Neurosurgery |
مقاله انگلیسی |
10 |
Using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely
استفاده از یادگیری ماشین و بینایی ماشین برای برآورد سرعت زاویه ای توربین های بادی در شبکه های هوشمند از راه دور-2021 Today, power generation from clean and renewable resources such as wind and solar is of great salience. Smart grid technology efficiently responds to the increasing demand for electric power. Intelligent monitoring, control, and maintenance of wind energy facilities are indispensable to increase the performance and efficiency of smart grids (SGs). Integration of state-of-the-art machine learning algorithms and vision sensor networks approaches pave the way toward enhancing the wind farms’ performance. The generating power in a wind turbine farm is the most critical parameter that should be measured accurately. Produced power is highly related to weather patterns, and a new farm in a near area is also likely to have similar energy generation. Therefore, accurate and perpetual prediction models of the existing wind farms can be led to develop new stations with lower costs. The paper aims to estimate the angular velocity of turbine blades using vision sensors and signal processing. The high wind in the wind farm can cause the camera to vibrate in successive frames, and the noise in the input images can also strengthen the problem. Thanks to couples of solid computer vision algorithms, including FAST (Features from Accelerated Segment Test), SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features), BF (Brute-Force), FLANN (Fast Library for Approximate Nearest Neighbors), AE (Autoencoder), and SVM (support vector machines), this paper accurately localizes the Hub and track the presence of the Blade in consecutive frames of a video stream. The simulation results show that determining the hub location and the blade presence in sequential frames results in an accurate estimation of wind turbine angular velocity with 95.36% accuracy.© 2021 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Machine vision | Blade detection | Image classification | Signal processing | Wind turbine | Smart grids |
مقاله انگلیسی |