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Heat transfer optimization and sensitivity analysis of Marangoni convection in nanoliquid with nanoparticle interfacial layer and cross-diffusion effects
Heat and mass transfer induced by Marangoni forces occur frequently in crystal growth and heat pipes, especially in microgravity situations. Therefore, the heat and mass transfer optimization in the thermosolutal Marangoni boundary layer flow of a nanomaterial with cross-diffusion effects is carried out in this study. Thermal radiation, magnetic field, and cross-diffusion are also incorporated in the thermal phenomena. The flow fields with nanolayer and without it are compared. The nanoparticle interfacial layer aspect accounted for in the nanofluid model makes the modeling more realistic. The optimization procedure is based on the Response Surface Methodology (RSM) model that utilizes the face-centered Central Composite Design (fc-CCD). The external constraining factors of the system like thermal radiation, magnetic field, and nanoparticle loading are explored for interactive impacts. The sensitivity of the heat and mass transfer is scrutinized. The interfacial layer aspect leads to an enhanced magnitude of the temperature field whereas the effect on the concentration profile is negligible. The inclination of the magnetic field augments the flow profiles significantly. The highest sensitivity of the heat and mass transfer is towards the thermal radiation aspect. The optimized output of heat and transfer rate is estimated to be when R = 1.6639, M = 1, and ? = 1 %. 2021 Elsevier Ltd -
Inclined magnetic field and nanoparticle aggregation effects on thermal Marangoni convection in nanoliquid: A sensitivity analysis
The heat transfer rate of thermal Marangoni convection in ethylene glycol-based titanium nanoliquid is analyzed by using the Response Surface Methodology (RSM). Two different heat sources (i.e. the temperature-related heat source (THS) and the space-related exponential heat source (ESHS)) are included in the thermal analysis. Aggregation of nanoparticles and inclined magnetism are also considered. The modified Krieger-Dougherty model and the modified Maxwell-Bruggeman model are used to analyze the aggregation aspect of the nanoparticles. The resulting nonlinear system is treated numerically by using the finite difference method. The sensitivity of the heat transfer rate to the thermal radiation parameter, the ESHS parameter, and the THS parameter is examined by using the RSM model. The individual impact of the actual parameters on various flow fields is compared and visualized by graphs. The heat transfer rate is positively sensitive to thermal radiation and negatively sensitive to the parameters of the heat source. Besides, the ESHS aspect has a greater impact on the heat transfer rate than the THS aspect. The velocity flow field is decelerated significantly (5.31%near the interface) by the magnetic field inclination angle. 2020 The Physical Society of the Republic of China (Taiwan) -
Sensitivity analysis of Marangoni convection in TiO2EG nanoliquid with nanoparticle aggregation and temperature-dependent surface tension
The sensitivity analysis of the magnetohydrodynamic thermal Marangoni convection of ethylene glycol (EG)-based titania (TiO2) nanoliquid is carried out by considering the effect of nanoparticle aggregation. The rate of heat transfer is explored by utilizing response surface methodology and estimating the sensitivity of the heat transfer rate toward the effective parameters: radiation parameter (1 ? R ? 3), magnetic parameter (1 ? M ? 3) and nanoparticle volume fraction (1 % ? ?? 5 %). The heat transfer phenomenon is scrutinized with thermal radiation and variable temperature at the surface. The effective thermal conductivity and viscosity with aggregation are modeled by using the MaxwellBruggeman and KriegerDougherty models. The governing equations are solved by using the apposite similarity transformations. It is found that when the effect of aggregation is considered, the velocity profile is lower. A positive sensitivity of the Nusselt number toward thermal radiation is observed. Further, a negative sensitivity of the heat transfer rate is observed toward the magnetic field and nanoparticle volume fraction. 2020, Akadiai Kiad Budapest, Hungary. -
Logistic growth and SIR modelling of coronavirus disease (COVID-19) outbreak in India: Models based on real-time data
The logistic growth model and the Susceptible-Infectious-Recovered (SIR) framework are utilized for the mathematical modelling of the Coronavirus disease (COVID-19) outbreak in India. Karnataka, Kerala and Maharashtra, three states of India, are selected based on the pattern of the disease spread and the prominence in being affected in India. The parameters of the models are estimated by utilizing real-time data. The models predict the ending of the pandemic in these states and estimate the number of people that would be affected under the prevailing conditions. The models classify the pandemic into five stages based on the nature of the infection growth rate. According to the estimates of the models it can be concluded that Kerala is in a stable situation whereas the pandemic is still growing in Karnataka and Maharashtra. The infection rate of Karnataka and Kerala are lesser than 5% and reveal a downward trend. On the other hand, the infection rate and the high predicted number of infectives in Maharashtra calls for more preventive measures to be imposed in Maharashtra to control the disease spread. The results of this analysis provide valuable information regarding the disease spread in India. 2020, International Information and Engineering Technology Association. -
Time-Dependent Nonlinear Convective Flow and Radiative Heat Transfer of Cu-Al2O3-H2O Hybrid Nanoliquid with Polar Particles Suspension: a Statistical and Exact Analysis
The statistical and exact analysis of heat transfer rate and skin friction coefficient of a nonlinear convective flow of Cu ? Al2O3 ? H2O hybrid nanofluid with polar particle suspension is performed. The heat transport phenomenon includes radiative heat effect. A micropolar fluid model is accounted. Exact solutions to the governing problem are found via Laplace transform method (LTM). The heat transfer rate and skin friction are analysed critically via statistical methods like probable error and regression models. The slope of linear regression of data points for skin friction and Nusselt number is estimated to quantify the increase/decrease. The Nusselt number and thermophysical properties for twenty-four different hybrid nanofluids are presented. A novel idea of a nonlinear convective flow of Cu ? Al2O3 ? H2O hybrid nanofluid with polar particle suspension is investigated for the first time. Opposite behaviour of velocity and microrotation profile are established when the physical parameters are varied. 2019, Springer Science+Business Media, LLC, part of Springer Nature. -
Sensitivity analysis of radiative heat transfer in Casson and nano fluids under diffusion-thermo and heat absorption effects
The exact analysis of the magnetohydrodynamic flow of a Newtonian nanofluid past an inclined plate through a porous medium is carried out. The flows of a Newtonian nanofluid and a non-Newtonian Casson fluid are juxtaposed. The heat transport phenomenon is analyzed in the presence of Dufour and heat absorption effects. The Darcy model and Rosseland approximation are employed to simulate the effects of porous media and radiative heat. The exact solutions are obtained by using the Laplace transform method. The effects of different physical parameters on the velocity, temperature and concentration profiles are scrutinized using graphs. Statistical techniques, like the slope of data points, coefficient of correlation, probable error, and multiple linear regression, are employed to analyze the rate of heat transfer and skin friction coefficient. Further, the sensitivity of the skin friction coefficient and Nusselt number are analyzed using the Response Surface Methodology (RSM). The Nusselt number has a positive sensitivity towards thermal radiation, and it is negatively sensitive towards nanoparticle volume fraction and Dufour number. 2019, SocietItaliana di Fisica / Springer-Verlag GmbH Germany, part of Springer Nature. -
Exact and statistical computations of radiated flow of nano and Casson fluids under heat and mass flux conditions
The statistical and exact analysis of the unsteady radiative flow of Nano and Casson fluids past a vertical plate with Dufour effect is carried out. The heat transport phenomenon is studied under uniform heat flux (UHF) and uniform wall temperature (UWT) conditions. The exact solution to the problem is found using the Laplace transform method (LTM). The effects of various parameters on velocity, temperature and concentration profiles are examined via graphs. The heat transfer rate and skin friction are analyzed through statistical tools like probable error and regression. The Dufour effect enhances the velocity and temperature profiles. It is also observed that the velocity profile is slightly greater in the case of UWT than the UHF case for both nanofluid and Casson fluid. From the regression analysis, it is established that the Dufour number and nanoparticle volume fraction have a negative impact whereas the radiative heat parameter has a positive impact on the rate of heat transfer. 2019 Society for Computational Design and Engineering -
Heat transport in the flow of magnetized nanofluid over a stretchable surface with heat sources: A mathematical model with realistic conditions
Analyzing the heat transport of nanofluids is of prime importance to various industrial and engineering sectors which involves modeling the physical phenomena via highly nonlinear partial differential equations. In this study, the flow and heat transport of a nanoliquid on a bi-directionally elongating surface subject to two different heat modulations (linear temperature-related heat source and space-related exponential heat source) is investigated using the two-component Buongiorno nanoliquid model. The dynamics of the nanoliquid are altered by an external magnetic field applied perpendicular to the sheet. The impact of Brownian motion, Lorentz forces, and thermophoresis are analyzed under the realistic passive control of the nanoparticles. A comparative analysis of the flow over the linear and nonlinear drawn surface is presented. Numeric solutions for the governing partial differential system are obtained through the finite difference method (FDM). Among two types of heat source modulations, the maximum heat transport is observed in the presence of the exponential space-based heat source modulation. The flow and thermal fields are found to advance in the linear elongated surface flow than the nonlinear elongated flow. Furthermore, the random movement of the nanoparticles and the greater magnitude of the Lorentz force have a positive effect on the thermal enhancement in the nanoliquid system. The results of the study have applications in heating/cooling processes, nanoliquid-dependent structures, and thermal systems with stretchable materials. 2021 Wiley-VCH GmbH -
Sensitivity Analysis of Heat Transport in Nanofluids with Marangoni Convection
Crystal growth, soap flm stabilization, coating processes, and growth of silicon newlinewafers involve Marangoni convective and#64258;ows. In microgravity situations, Marangoni effect is more prominent than gravity-induced buoyancy forces. In such situations, the convective and#64258;ows in the and#64258;uids will be driven by surface tension gradients. Moreover, the control of heat transport in the hydromagnetic semiconductor crystals involves Marangoni convection. Therefore, the heat transport rate in Marangoni convective and#64258;ow of nanoand#64258;uids is optimized in this research work. The thermal, thermo-solutal, mixed thermo-solutal Marangoni convection problems are explored in the presence of an external magnetic feld. The thermal phenomenon is scrutinized by including thermal radiation. Diand#64256;erent external eand#64256;ects are included in the problems and a detailed parametric analysis is carried out by using graphical visualizations. The newlinegoverning equations are constructed by utilizing the conservation equations of mass, newlinemomentum, energy and concentration. Realistic nanoand#64258;uid models are chosen which are validated with experimental data. Finite-diand#64256;erence-based and Runge-Kuttabased solving methodologies are adopted. The optimization of the heat (and mass) transport is carried out using the Response Surface Methodology (RSM). The facecentered central composite design is used for optimization. The quadratic empirical models obtained are further explored by estimating the sensitivity. The problem studied in each chapter is given below: Thermal Marangoni and#64258;ow of a nanoand#64258;uid with nanoparticle aggregation newlineA study of magnetohydrodynamic thermal Marangoni convection of ethylene glycol (EG) based titania (TiO2) nanoand#64258;uid is carried out by considering the eand#64256;ect of nanoparticle aggregation. The heat transport phenomenon is scrutinized with thermal radiation. The eand#64256;ective thermal conductivity and viscosity with aggregation are modeled by using the Maxwell-Bruggeman and Krieger-Dougherty models. -
BUSINESS RESPONSES TOWARDS CORPORATE SOCIAL RESPONSIBILITY AND SUSTAINABLE DEVELOPMENT GOALS DURING COVID-19 PANDEMIC; [RESPOSTAS DAS EMPRESAS EM RELAO RESPONSABILIDADE SOCIAL CORPORATIVA E METAS DE DESENVOLVIMENTO SUSTENTEL DURANTE A PANDEMIA DE COVID-19]
Objective: In India, incorporating structural transformation in corporate social responsibility for achieving sustainable development goals in the Covid-19 Pandemic has become a priority. Therefore, the present article aims to review the corporate social responsibility activities conducted by Nine Indian companies (two public sector organizations and seven private sector organizations) towards attaining Sustainable Development Goals in times of global pandemic. To explore CSR activities performed by the Indian companies (both public and private) in recent times of crisis of covid-19. To assess the impact of Covid-19 on the economic, social, and technological environment of Indian Companies in recent times. Method: For our research, we chose India since it was one of the earliest and worst-hit countries during the recent pandemic. Even though huge Asian corporations are a minority, It is a good fit for our study goal for various reasons. Less and medium-sized businesses, on the other hand, have a far smaller influence on society, Due to their prominence in the press, clients or consumers are more likely to provide feedback if they are proactive. As a second point, major companies have greater resources (e.g., human and financial) to spend on reporting and distributing social and environmental information. The existing study applied bibliometric analysis in the exploratory research. The data collection done through secondary sources in which articles extracted with the use of PRISMA flowchart. Result: The study's findings state that most selected Indian Companies spend Rupees 50-1000 crores as corporate social responsibility activities and contribute to the Pradhan Mantri Relief Fund as a business response to face challenges in times of pandemic. Moreover, private companies spend more on CSR activities than public companies in India. The study provides suggestions to the government to make the corporate social responsibility activities compulsory for all the profitable companies so that country can maintain a corporate pool of contingent funds that can utilize to meet such times of critical circumstances. Conclusion: Companies are taking steps to ensure the safety and protection of their personnel. This has been reflected in corporate social responsibility initiatives as well. Corporate social responsibility efforts must include pushing for and facilitating access to health insurance programmes and other equity indicators as a result. 2023 The Author(s). -
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Creativity Education in India: Breaking Barriers
Classrooms in India are posed with challenges of rigid curriculum demands, lack of resources, large class sizes and very often low motivation levels among teachers. Add to that poor teacher training facilities. Given the constraints, the pursuit of encouraging creativity education in the Indian schooling system seems like a tall order. The aim of this chapter is to explore the scope for creative learning and expression in Indian classrooms. The purpose is also to search for education models that facilitate and encourage creativity amongst children by adapting the curriculum to their advantage and operate within the constraints imposed by systemic drawbacks. The chapter searches for a coherent, pragmatic definition of creativity among practitioners and pedagogues with the rationale of identifying workable solutions for dissemination of creativity education in Indian classrooms. The methodology entails conversations and interviews with teachers, curriculum planners and pedagogues representing different education models and schooling systems in India. A case-study design approach is assumed to present programs that are successfully implementing practices which encourage creativity learning among their students. An attempt is made to highlight the philosophical beliefs that underlie adoption of such practices as well as to document the challenges and demands that such an endeavour imposes on the system. The chapter therefore, provides an optimistic view of the future of out of the box learning not only in the Indian classrooms but also makes a case for all education systems stifled by curriculum constraints and lack of resources. Inter-Disciplinary Press 2012. -
A Novel Back-Propagation Neural Network for Intelligent Cyber-Physical Systems for Wireless Communications
Wireless sensor networks, which play a significant role in monitoring complex environments that change rapidly over time, were used in the Artificial Intelligence method. External factors or the device designers themselves are both responsible for this complex behavior. Sensor networks often use machine learning techniques to adapt to such conditions, eliminating the need for excessive redesign. Cyber-physical systems (CPS) appeared as the promising option for improving physical-virtual interactions. The quality of the system containing processing information is primarily determined by the system function. There are many benefits obtained while combining Artificial Intelligence (AI) and Cyber-Physical Systems (CPSs) in buildings. In CPS-based indoor environment has various design schemes containing measurement and intelligent buildings in the control system consisting of detection, tracking, execution, and communication modules. The Multi-Agent System (MAS) is the smallest control unit that simulates among neurons and it flexibly provides the information. To mimic the interactions between human neurons, multi-agents are used. In this paper, the CPSs information world is built on the fundamental principle of granular formal concepts and the theory of granular computing is investigated. The calculation module is used by Back-Propagation Neural Network (BPNN) for pattern recognition and classification by environmental information. Various parameters namely the normalized root mean square error, peak signal-to-noise ratio, mean square error, and the mean absolute error are chosen as the objective assessment criteria to assess the benefits of the proposed method and the effectiveness of the proposed system is proven. 2024 IETE. -
Utilizing Deep Learning Techniques for Lung Cancer Detection
Deep learning can extract meaningful insights from complex biomedical statistics, which includes Radiographs and virtual tomosynthesis. Traits in contemporary deep studying architectures have enabled faster and more correct mastering of the functions gifted in clinical imagery, main to better accuracy and precision in medical analysis and imaging. Deep studying strategies may be used to pick out patterns within the pics which may be indicative of illnesses like lung cancer. Those ailment patterns, which include small lung nodules, can be used for early detection and prognosis of the sickness. Recent studies have employed deep learning strategies consisting of Convolutional Neural Networks (CNNs) and switch learning to come across most lung cancers in CT pictures. The first step in this manner is to generate datasets of pictures of the lungs, each from wholesome people and those with most lung cancers. Those datasets can then be used to teach a deep knowledge of a set of rules that may be optimized to it should locate those styles. Once educated, the version can be used to come across styles indicative of lung most cancers from new take a look at images with high accuracy. For further accuracy and reliability, extra up-processing techniques, along with segmentation and records augmentation, may be used. Segmentation can be used to detect a couple of lung nodules in a photo, and records augmentation can be used to lessen fake high quality outcomes. 2024 IEEE. -
Synthesis and Characterization of Cyclopentadithiophene and Thienothiophene-Based Polymers for Organic Thin-Film Transistors and Solar Cells
Novel donor-donor type alternating copolymers (8CDT-TT and 16CDT-TT) derived from cyclopentadithiophene (CDT) and thienothiophene (TT) moieties that differ from solubilizing side chains were successfully synthesized and characterized. After the synthesis of CDT-TT-based conjugated polymers with dioctyl and dihexadecyl side chains, their optical, thermal, structural and semiconducting properties were investigated. Organic thin-film transistors fabricated from 8CDT-TT and 16CDT-TT exhibit carrier mobilities as high as 3.920-4 and 1.050-3 cm2V-1s-1, respectively. Bulk heterojunction solar cells fabricated using a polymer:PCBM blend ratio of 1:3 exhibit power conversion efficiencies of 2.12 and 1.84% for 8CDT-TT and 16CDT-TT, respectively. 2018, The Polymer Society of Korea and Springer Nature B.V. -
CDADITagger: An Approach Towards Content Based Annotations Driven Image Tagging Integrating Hybrid Semantics
Considering the rapid growth of multimedia data, especially images, image tagging is considered the most efficient way to organize or retrieve images. The significance of image tagging is growing extensively but the frameworks employed for tagging these images aren't sophisticated. These images aren't properly tagged because of a lack of resources for tagging or manual tagging is a challenging task considering such voluminous data. Already existing frameworks take both the image data and tag-related textual data but ultimately resulted in mediocre or unpalatable performance as they are dataset centered. To overcome these limitations in existing frameworks we proposed an image tagging mechanism, CDADITagger capable of automatically tagging images efficiently and much more reliable compared to existing frameworks. This framework can tackle real-world applications like tagging a new unknown image as the framework isn't powered by dataset alone but is designed to inculcate images from search engines like Google, Bing, etc. to have comprehensive knowledge of real-time data. These images are classified using CNN and tag-related textual data is classified using decision trees for enhanced performance. While tagging images from the classified tags, are sorted based on the semantic computation values, only the top 50% of the instances classified are selected. The tags which are more correlated to the image are ranked and finalized. The proposed semantically inclined framework CDADITagger outshined the well-established frameworks with an accuracy of 96.60% and a precision of 95.84% making it a more reliable approach. 2022 IEEE. -
Simulation and Experimental Analysis of L-Section in Reinforced Cement Concrete: Uncertainties in Performance and Strength
The design and construction of reinforced cement concrete (RCC) flooring play a crucial role in the overall stability of a structure, particularly in regions prone to tectonic activity. RCC floors comprise various beams, including intermediate T-sections and specific L-sections at critical points such as corners and around staircases or lift openings. This paper identifies a key challenge in building frameworks to resist tectonic loads. It further explores the components of the structure that provide potential for interruption, capability, and the safe transfer of tectonic loading to the array connection, all while maintaining sufficient strength. The L-sections were experimented on using various grades of concrete and sizes to reinforce connections under diverse loading conditions. L-sections contribute to reducing floor height, solving economic and technical problems, and creating advanced composite connections that integrate the proposed structural system. The analysis was conducted both analytically and experimentally to assess methods to resist earthquake forces based on stiffness, building strength, and elasticity capacity. These approaches have been identified to safeguard buildings during substantial seismic events. The development of the L-section is detailed, highlighting the loading process and the capacity to overcome various structural challenges. 2024 by the authors. Licensee MDPI, Basel, Switzerland. -
System Design for Financial and Economic Monitoring Using Big Data Clustering
Economic data executives are becoming increasingly important for the longevity and improvement of ventures due to the constant expansion in the influence of data innovation. This study lays out an undertaking economic data the executive's structure for the intricate internal undertaking economic data the board business. It also includes the application of web-based big data technology to understand the fairness, reliability, and security of system database calculations, mainly to improve office capabilities and solve daily project management problems. used in the project. The aim is to evaluate the suitability of transfer clustering computation (DCA) for managing large amounts of data in energy systems and the suitability of data economics dispatch methods for harnessing new energies. Then, combine day-ahead shipping plans with continuous shipping plans to create a multi-period, data-economic shipping model. Consider how the calculations are performed using a case study on the use of new energies. This will enable new energy in multi-period data economics shipping models while meeting his DR requirements on the customer side. 2023 IEEE. -
AI-Controlled Wind Turbine Systems: Integrating IoT and Machine Learning for Smart Grids
Advances in renewable energy technologies are pivotal in addressing the challenges posed by the depletion of traditional energy sources and their associated environmental impacts. Among these, wind energy stands out as a promising avenue, with wind turbine farms proliferating globally. However, the unpredictable nature of wind and intricate interplay between turbines necessitate innovative solutions for efficient operation and maintenance. This paper reviews advancements in intelligent control systems, notably those proposed by Smart Wind technologies. These systems leverage a network of sensors and IoT devices to gather real-time data, such as wind speed, temperature, and humidity, to optimize turbine performance. A significant focus is on turbines employing doubly-fed induction generators, which offer benefits like adjustable speed and consistent frequency operation. Their integration into smart grids introduces challenges concerning power system dynamics'security and reliability. This review delves into the dynamics, characteristics, and potential instabilities of such integrations, emphasizing the uncertainties in wind and nonlinear load predictions. A noteworthy finding is the rising prominence of artificial intelligence, particularly machine and deep learning, in predictive diagnostics. These methodologies offer costeffective, accurate, and efficient solutions, holding potential for enhancing power system stability and accuracy in the smart grid context. The Authors, published by EDP Sciences, 2024. -
Automatic Classification of Normal and Affected Vegetables Based on Back Propagation Neural Network and Machine Vision
This article presents a neural network and machine vision-based approach to classify the vegetables as normal or affected. The farmers will have great difficulty if there is a change from one disease control to another. The examination through an open eye to classify the diseases by name is more expensive. The texture and color features are used to identify and classify different vegetables into normal or affected using a neural network and machine vision. The mixture of both the features is proved to be more effective. The results of experiments show that the proposed methodology extensively supports the accuracy in automatic detection of affected and normal vegetables. The applications in packing and grading of vegetables are the outcome of this research article. 2019, Springer Nature Singapore Pte Ltd.