Search results for: computational pipeline

Authors: Lina Wu, Ye Li, Jia Liu

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We are interested in solving Liouville-type problems to explore constancy properties for maps or differential forms on Riemannian manifolds. Geometric structures on manifolds, the existence of constancy properties for maps or differential forms, and energy growth for maps or differential forms are intertwined. In this article, we concentrate on discovery of solutions to Liouville-type problems where manifolds are Euclidean spaces (i.e. flat Riemannian manifolds) and maps become real-valued functions. Liouville-type results of vanishing properties for functions are obtained. The original work in our research findings is to extend the q-energy for a function from finite in L^q space to infinite in non-L^q space by applying p-balanced technique where q = p = 2. Calculation skills such as Hölder's Inequality and Tests for Series have been used to evaluate limits and integrations for function energy. Calculation ideas and computational techniques for solving Liouville-type problems shown in this article, which are utilized in Euclidean spaces, can be universalized as a successful algorithm, which works for both maps and differential forms on Riemannian manifolds. This innovative algorithm has a far-reaching impact on research work of solving Liouville-type problems in the general settings involved with infinite energy. The p-balanced technique in this algorithm provides a clue to success on the road of q-energy extension from finite to infinite.

Keywords: differential forms, holder inequality, Liouville-type problems, p-balanced growth, p-harmonic maps, q-energy growth, tests for series

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Authors: Mouhamadou Diop, Mohamed I. Hassan

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Molten metallic flow in metallurgical plant is highly turbulent and presents a complex coupling with heat transfer, phase transfer, chemical reaction, momentum transport, etc. Molten silicon flow has significant effect in directional solidification of multicrystalline silicon by affecting the temperature field and the emerging crystallization interface as well as the transport of species and impurities during casting process. Owing to the complexity and limits of reliable measuring techniques, computational models of fluid flow are useful tools to study and quantify these problems. The overall objective of this study is to investigate the potential of a traveling magnetic field for an efficient operating control of the molten metal flow. A multidimensional numerical model will be developed for the calculations of Lorentz force, molten metal flow, and the related phenomenon. The numerical model is implemented in a laboratory-scale silicon crystallization furnace. This study presents the potential of traveling magnetic field approach for an efficient operating control of the molten flow. A numerical model will be used to study the effects of magnetic force applied on the molten flow, and their interdependencies. In this paper, coupled and decoupled, steady and unsteady models of molten flow and crystallization interface will be compared. This study will allow us to retrieve the optimal traveling magnetic field parameter range for crystallization furnaces and the optimal numerical simulations strategy for industrial application.

Keywords: multidimensional, numerical simulation, solidification, multicrystalline, traveling magnetic field

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Authors: Alia Alghosoun, Ashraf Osman, Mohammed Seaid

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A coupled two-layer finite volume/finite element method was proposed for solving dam-break flow problem over deformable beds. The governing equations consist of the well-balanced two-layer shallow water equations for the water flow and a linear elastic model for the bed deformations. Deformations in the topography can be caused by a brutal localized force or simply by a class of sliding displacements on the bathymetry. This deformation in the bed is a source of perturbations, on the water surface generating water waves which propagate with different amplitudes and frequencies. Coupling conditions at the interface are also investigated in the current study and two mesh procedure is proposed for the transfer of information through the interface. In the present work a new procedure is implemented at the soil-water interface using the finite element and two-layer finite volume meshes with a conservative distribution of the forces at their intersections. The finite element method employs quadratic elements in an unstructured triangular mesh and the finite volume method uses the Rusanove to reconstruct the numerical fluxes. The numerical coupled method is highly efficient, accurate, well balanced, and it can handle complex geometries as well as rapidly varying flows. Numerical results are presented for several test examples of dam-break flows over deformable beds. Mesh convergence study is performed for both methods, the overall model provides new insight into the problems at minimal computational cost.

Keywords: dam-break flows, deformable beds, finite element method, finite volume method, hybrid techniques, linear elasticity, shallow water equations

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Authors: Alireza Rahimi, Abdolreza Farhadian, Arash Tajik, Elaheh Sadeh, Avni Berisha, Esmaeil Akbari Nezhad

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Although natural polymers have been shown to have some inhibitory properties on sour corrosion, they are not considered very effective green corrosion inhibitors. Accordingly, effective corrosion inhibitors should be developed based on natural resources to mitigate sour corrosion in the oil and gas industry. Here, Arabic gum was employed as an eco-friendly precursor for the synthesis of innovative polyurethanes designed as highly efficient corrosion inhibitors for sour oilfield solutions. A comprehensive assessment, combining experimental and computational analyses, was conducted to evaluate the inhibitory performance of the inhibitor. Electrochemical measurements demonstrated that a concentration of 200 mM of the inhibitor offered substantial protection to mild steel against sour corrosion, yielding inhibition efficiencies of 98% and 95% at 25 ºC and 60 ºC, respectively. Additionally, the presence of the inhibitor led to a smoother steel surface, indicating the adsorption of polyurethane molecules onto the metal surface. X-ray photoelectron spectroscopy results further validated the chemical adsorption of the inhibitor on mild steel surfaces. Scanning Kelvin probe microscopy revealed a shift in the potential distribution of the steel surface towards negative values, indicating inhibitor adsorption and corrosion process inhibition. Molecular dynamic simulation indicated high adsorption energy values for the inhibitor, suggesting its spontaneous adsorption onto the Fe (110) surface. These findings underscore the potential of Arabic gum as a viable resource for the development of polyurethanes under mild conditions, serving as effective corrosion inhibitors for sour solutions.

Keywords: environmental effect, Arabic gum, corrosion inhibitor, sour corrosion, molecular dynamics simulation

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Authors: Yuchen Yang, Zhenming Wang, Jun Zhu, Ning Zhao

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An easy-to-implement and robust finite volume multi-resolution Weighted Essentially Non-Oscillatory (WENO) scheme is proposed on adaptive cartesian grids in this paper. Such a multi-resolution WENO scheme is combined with the ghost cell immersed boundary method (IBM) and wall-function technique to solve Navier-Stokes equations. Unlike the k-exact finite volume WENO schemes which involve large amounts of extra storage, repeatedly solving the matrix generated in a least-square method or the process of calculating optimal linear weights on adaptive cartesian grids, the present methodology only adds very small overhead and can be easily implemented in existing edge-based computational fluid dynamics (CFD) codes with minor modifications. Also, the linear weights of this adaptive finite volume multi-resolution WENO scheme can be any positive numbers on condition that their sum is one. It is a way of bypassing the calculation of the optimal linear weights and such a multi-resolution WENO scheme avoids dealing with the negative linear weights on adaptive cartesian grids. Some benchmark viscous problems are numerical solved to show the efficiency and good performance of this adaptive multi-resolution WENO scheme. Compared with a second-order edge-based method, the presented method can be implemented into an adaptive cartesian grid with slight modification for big Reynolds number problems.

Keywords: adaptive mesh refinement method, finite volume multi-resolution WENO scheme, immersed boundary method, wall-function technique.

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Authors: H. Asadi, H. Naderan Tahan

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The aim of this paper is to investigate the effect of geometrical properties of helical fins in double pipe heat exchangers. On the other hand, the purpose of this project is to derive the hydraulic and thermal design tables and equations of double heat exchangers with helical fins. The numerical modeling is implemented to calculate the considered parameters. Design tables and correlated equations are generated by repeating the parametric numerical procedure for different fin geometries. Friction factor coefficient and Nusselt number are calculated for different amounts of Reynolds, fluid Prantle and fin twist angles for the range of laminar fluid flow in annular tube with helical fins. Results showed that friction factor coefficient and Nusselt number will be increased for higher Reynolds numbers and fins’ twist angles in general. These two parameters follow different patterns in response to Reynolds number increment. Thermal performance factor is defined to analyze these different patterns. Temperature and velocity contours are plotted against twist angle and number of fins to describe the changes in flow patterns in different geometries of twisted finned annulus. Finally twisted finned annulus friction factor coefficient, Nusselt Number and thermal performance factor are correlated by simulating the model in different design points.

Keywords: double pipe heat exchangers, heat exchanger performance, twisted fins, computational fluid dynamics

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Authors: Vatsal M. Patel, Navin B. Patel

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The present studies deal with the developing a series bearing a diphenyl ethers nucleus using structure-based drug design concept. A newer series of diphenyl ether based azetidinone namely N-(3-chloro-2-oxo-4-(3-phenoxyphenyl)azetidin-1-yl)-2-(substituted amino)acetamide (2a-j) have been synthesized by condensation of m-phenoxybenzaldehyde with 2-(substituted-phenylamino)acetohydrazide followed by the cyclisation of resulting Schiff base (1a-j) by conventional method as well as microwave heating approach as a part of an environmentally benign synthetic protocol. All the synthesized compounds were characterized by spectral analysis and were screened for in vitro antimicrobial, antitubercular and antiprotozoal activity. The compound 2f was found to be most active M. tuberculosis (6.25 µM) MIC value in the primary screening as well as this same derivative has been found potency against L. mexicana and T. cruzi with MIC value 2.09 and 6.69 µM comparable to the reference drug Miltefosina and Nifurtimox. To provide understandable evidence to predict binding mode and approximate binding energy of a compound to a target in the terms of ligand-protein interaction, all synthesized compounds were docked against an enoyl-[acyl-carrier-protein] reductase of M. tuberculosis (PDB ID: 4u0j). The computational studies revealed that azetidinone derivatives have a high affinity for the active site of enzyme which provides a strong platform for new structure-based design efforts. The Lipinski’s parameters showed good drug-like properties and can be developed as an oral drug candidate.

Keywords: antimycobacterial, antiprotozoal, azetidinone, diphenylether, docking, microwave

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Authors: Anna Keuchenius

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Broadly speaking, scientific development is studied in either a qualitative manner with a focus on the behavior and interpretations of academics, such as the sociology of science and science studies or in a quantitative manner with a focus on the analysis of publications, such as scientometrics and bibliometrics. Both come with a different set of methodologies and few cross-references. This paper contributes to the bridging of this divide, by on the on hand approaching the process of scientific progress from a qualitative sociological angle and using on the other hand quantitative and computational techniques. As a case study, we analyze the diffusion of Granovetter's hypothesis from his 1973 paper 'On The Strength of Weak Ties.' A network is constructed of all scientists that have referenced this particular paper, with directed edges to all other researchers that are concurrently referenced with Granovetter's 1973 paper. Studying the structure and growth of this network over time, it is found that Granovetter's hypothesis is used by distinct communities of scientists, each with their own key-narrative into which the hypothesis is fit. The diffusion within the communities shares similarities with the diffusion of an innovation in which innovators, early adopters, and an early-late majority can clearly be distinguished. Furthermore, the network structure shows that each community is clustered around one or few hub scientists that are disproportionately often referenced and seem largely responsible for carrying the hypothesis into their scientific subfield. The larger implication of this case study is that the diffusion of scientific hypotheses and ideas are not the spreading of well-defined objects over a network. Rather, the diffusion is a process in which the object itself dynamically changes in concurrence with its spread. Therefore it is argued that the methodology presented in this paper has potential beyond the scientific domain, in the study of diffusion of other not well-defined objects, such as opinions, behavior, and ideas.

Keywords: diffusion of innovations, network analysis, scientific development, sociology of science

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Authors: Meriem Bahij, Ahmed Nafidi, Boujemâa Achchab, Sílvio M. A. Gama, José A. O. Matos

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Stochastic modeling concerns the use of probability to model real-world situations in which uncertainty is present. Therefore, the purpose of stochastic modeling is to estimate the probability of outcomes within a forecast, i.e. to be able to predict what conditions or decisions might happen under different situations. In the present study, we present a model of a stochastic diffusion process based on the bi-Weibull distribution function (its trend is proportional to the bi-Weibull probability density function). In general, the Weibull distribution has the ability to assume the characteristics of many different types of distributions. This has made it very popular among engineers and quality practitioners, who have considered it the most commonly used distribution for studying problems such as modeling reliability data, accelerated life testing, and maintainability modeling and analysis. In this work, we start by obtaining the probabilistic characteristics of this model, as the explicit expression of the process, its trends, and its distribution by transforming the diffusion process in a Wiener process as shown in the Ricciaardi theorem. Then, we develop the statistical inference of this model using the maximum likelihood methodology. Finally, we analyse with simulated data the computational problems associated with the parameters, an issue of great importance in its application to real data with the use of the convergence analysis methods. Overall, the use of a stochastic model reflects only a pragmatic decision on the part of the modeler. According to the data that is available and the universe of models known to the modeler, this model represents the best currently available description of the phenomenon under consideration.

Keywords: diffusion process, discrete sampling, likelihood estimation method, simulation, stochastic diffusion process, trends functions, bi-parameters weibull density function

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Authors: Manendra Singh Parihar, Sandip Ghosh Chowdhury

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Austenitic stainless steels are widely used and give a good combination of properties. When this steel is plastically deformed, a phase transformation of the metastable Face Centred Cubic Austenite to the stable Body Centred Cubic (α’) or to the Hexagonal close packed (ԑ) martensite may occur, leading to the enhancement in the mechanical properties like strength. The work was based on variant selection and corresponding texture analysis for the strain induced martensitic transformation during deformation of the parent austenite FCC phase to form the product HCP and the BCC martensite phases separately, obeying their respective orientation relationships. The automated method for reconstruction of the parent phase orientation using the EBSD data of the product phase orientation is done using the MATLAB and TSL-OIM software. The method of triplets was used which involves the formation of a triplet of neighboring product grains having a common variant and linking them using a misorientation-based criterion. This led to the proper reconstruction of the pre-transformation phase orientation data and thus to its microstructure and texture. The computational speed of current method is better compared to the previously used methods of reconstruction. The reconstruction of austenite from ԑ and α’ martensite was carried out for multiple samples and their IPF images, pole figures, inverse pole figures and ODFs were compared. Similar type of results was observed for all samples. The comparison gives the idea for estimating the correct sequence of the transformation i.e. γ → ε → α’ or γ → α’, during deformation of AISI 304 austenitic stainless steel.

Keywords: variant selection, reconstruction, EBSD, austenitic stainless steel, martensitic transformation

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Authors: Ahmad Odeh

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Researchers are currently more concerned about the calculations of energy at the operational stage, mainly due to its larger environmental impact, but the fact remains, embodied energies represent a substantial contributor unaccounted for in the overall energy computation method. The calculation of materials’ embodied energy during the construction stage is complicated. This is due to the various factors involved. The equipment used, fuel needed, and electricity required for each type of materials varies with location and thus the embodied energy will differ for each project. Moreover, the method used in manufacturing, transporting and putting in place will have significant influence on the materials’ embodied energy. This anomaly has made it difficult to calculate or even bench mark the usage of such energies. This paper presents a model aimed at calculating embodied energies based on such variabilities. It presents a systematic approach that uses an efficient method of calculation to provide a new insight for the selection of construction materials. The model is developed in a BIM environment. The quantification of materials’ energy is determined over the three main stages of their lifecycle: manufacturing, transporting and placing. The model uses three major databases each of which contains set of the construction materials that are most commonly used in building projects. The first dataset holds information about the energy required to manufacture any type of materials, the second includes information about the energy required for transporting the materials while the third stores information about the energy required by machinery to place the materials in their intended locations. Through geospatial data analysis, the model automatically calculates the distances between the suppliers and construction sites and then uses dataset information for energy computations. The computational sum of all the energies is automatically calculated and then the model provides designers with a list of usable equipment along with the associated embodied energies.

Keywords: BIM, lifecycle energy assessment, building automation, energy conservation

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Authors: Sushil Kumar Singh, Ashok Kumar, Ankit Ganeshpurkar, Ravi Singh, Devendra Kumar

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In spectrum of neurodegenerative diseases, Alzheimer’s disease (AD) is characterized by the presence of amyloid β plaques and neurofibrillary tangles in the brain. It results in cognitive and memory impairment due to loss of cholinergic neurons, which is considered to be one of the contributing factors. Donepezil, an acetylcholinesterase (AChE) inhibitor which also inhibits butyrylcholinesterase (BuChE) and improves the memory and brain’s cognitive functions, is the most successful and prescribed drug to treat the symptoms of AD. The present work is based on designing of the selective BuChE inhibitors using computational techniques. In this work, machine learning models were trained using classification algorithms followed by screening of diverse chemical library of compounds. The various molecular modelling and simulation techniques were used to obtain the virtual hits. The amide derivatives of 4-(phenylsulfonamido) benzoic acid were synthesized and characterized using 1H & 13C NMR, FTIR and mass spectrometry. The enzyme inhibition assays were performed on equine plasma BuChE and electric eel’s AChE by method developed by Ellman et al. Compounds 31, 34, 37, 42, 49, 52 and 54 were found to be active against equine BuChE. N-(2-chlorophenyl)-4-(phenylsulfonamido)benzamide and N-(2-bromophenyl)-4-(phenylsulfonamido)benzamide (compounds 34 and 37) displayed IC50 of 61.32 ± 7.21 and 42.64 ± 2.17 nM against equine plasma BuChE. Ortho-substituted derivatives were more active against BuChE. Further, the ortho-halogen and ortho-alkyl substituted derivatives were found to be most active among all with minimal AChE inhibition. The compounds were selective toward BuChE.

Keywords: Alzheimer disease, butyrylcholinesterase, machine learning, sulfonamides

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Authors: Khaled M. Hama, Mohammed Al Magableh, Zaid Al Kuri, Ahmad Qayam

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The study aimed to identify the role of academic leaders at Jerash University in crisis management from the faculty members' point of view, ‘the emerging Corona pandemic as a model’, as well as to identify the differences in the role of academic leaders at Jerash University in crisis management at the significance level (0.05 ≤ α) according to the study variables Gender Academic rank, years of experience, and identifying proposals that contribute to developing the performance of academic leaders at Jerash University in crisis management, ‘the Corona pandemic as a model’. The study was applied to a randomly selected sample of (72) faculty members at Jerash University, The researcher designed a tool for the study, which is the questionnaire, and it included two parts: the first part related to the personal data of the study sample members, and the second part was divided into five areas and (34) paragraphs to reveal the role of academic leaders at Jerash University in crisis management - the Corona pandemic as a model, it was confirmed From the validity and reliability of the tool, the study used the descriptive analytical method The study reached the following results: that the role of academic leaders at Jerash University in crisis management from the point of view of faculty members, ‘the emerging corona pandemic as a model’, came to a high degree, and there were no statistically significant differences at the level of statistical significance (α = 0.05) between the computational circles for the estimates of individuals The study sample for the role of academic leaders at Jerash University in crisis management is attributed to the study variables (gender, academic rank, and years of experience)

Keywords: academic leaders, crisis management, corona pandemic, Jerash University

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Authors: Deyu Zhou, Xiao Xue, Lizhen Cui

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With the increasing collaboration among various services and the growing complexity of user demands, there are more and more factors affecting the stable development of the cloud manufacturing service economic system (CMSE). This poses new challenges to the evolution analysis of the CMSE. Many researchers have modeled and analyzed the evolution process of CMSE from the perspectives of individual learning and internal factors influencing the system, but without considering other important characteristics of the system's individuals (such as heterogeneity, bounded rationality, etc.) and the impact of external environmental factors. Therefore, this paper proposes an integrated artificial social model for the cloud manufacturing service economic system, which considers both the characteristics of the system's individuals and the internal and external influencing factors of the system. The model consists of three parts: the Agent model, environment model, and rules model (Agent-Environment-Rules, AER): (1) the Agent model considers important features of the individuals, such as heterogeneity and bounded rationality, based on the adaptive behavior mechanisms of perception, action, and decision-making; (2) the environment model describes the activity space of the individuals (real or virtual environment); (3) the rules model, as the driving force of system evolution, describes the mechanism of the entire system's operation and evolution. Finally, this paper verifies the effectiveness of the AER model through computational and experimental results.

Keywords: cloud manufacturing service economic system (CMSE), AER model, artificial social modeling, integrated framework, computing experiment, agent-based modeling, social networks

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Authors: A. Robak, M. Pieńko, E. Błazik-Borowa, J. Bęc, I. Szer

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The analyzes and classifications presented in the article were based on the research carried out in year 2016 and 2017 on a group of nearly one hundred scaffoldings assembled and used on construction sites in different parts of Poland. During scaffolding selection process efforts were made to maintain diversification in terms of parameters such as scaffolding size, investment size, type of investment, location and nature of conducted works. This resulted in the research being carried out on scaffoldings used for church renovation in a small town or attached to the facades of classic apartment blocks, as well as on scaffoldings used during construction of skyscrapers or facilities of the largest power plants. This variety allows to formulate general conclusions about the technical condition of used frame scaffoldings. Exploitation damages of the frame scaffolding elements were divided into three groups. The first group includes damages to the main structural components, which reduce the strength of the scaffolding elements and hence the whole structure. The qualitative analysis of these damages was made on the basis of numerical models that take into account the geometry of the damage and on the basis of computational nonlinear static analyzes. The second group focuses on exploitation damages such as the lack of a pin on the guardrail bolt which may cause an imminent threat to people using scaffolding. These are local damages that do not affect the bearing capacity and stability of the whole structure but are very important for safe use. The last group consider damages that reduce only aesthetic values and do not have direct impact on bearing capacity and safety of use. Apart from qualitative analyzes the article will present quantitative analyzes showing how frequently given type of damage occurs.

Keywords: scaffolding, damage, safety, numerical analysis

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: M. A. Alqahtani, D. P. Coleman, N. D. Pugh, L. D. M. Nokes

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In diagnostic ultrasound, the echo graphic B-scan texture is an important area of investigation since it can be analyzed to characterize the histological state of internal tissues. An important factor requiring consideration when evaluating ultrasonic tissue texture is the depth. The effect of attenuation with depth of ultrasound, the size of the region of interest, gain, and dynamic range are important variables to consider as they can influence the analysis of texture features. These sources of variability have to be considered carefully when evaluating image texture as different settings might influence the resultant image. The aim of this study is to investigate the effect of depth on the texture features in-vivo using a 3D ultrasound probe. The left leg medial head of the gastrocnemius muscle of 10 healthy subjects were scanned. Two regions A and B were defined at different depth within the gastrocnemius muscle boundary. The size of both ROI’s was 280*20 pixels and the distance between region A and B was kept constant at 5 mm. Texture parameters include gray level, variance, skewness, kurtosis, co-occurrence matrix; run length matrix, gradient, autoregressive (AR) model and wavelet transform were extracted from the images. The paired t –test was used to test the depth effect for the normally distributed data and the Wilcoxon–Mann-Whitney test was used for the non-normally distributed data. The gray level, variance, and run length matrix were significantly lowered when the depth increased. The other texture parameters showed similar values at different depth. All the texture parameters showed no significant difference between depths A and B (p > 0.05) except for gray level, variance and run length matrix (p < 0.05). This indicates that gray level, variance, and run length matrix are depth dependent.

Keywords: ultrasound image, texture parameters, computational biology, biomedical engineering

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Authors: Jadon Matthiasa, Cindy Donga, Ali Al Jibouria, Hsin Kuo

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The environmental impact of emissions from the Covanta Waste-to-Energy facility in Burnaby, BC, was comprehensively evaluated, focusing on the dispersion of air pollutants and the subsequent assessment of heavy metal contamination in surrounding soils. A Gaussian Plume Model, implemented in MATLAB, was utilized to simulate the dispersion of key pollutants to understand their atmospheric behaviour and potential deposition patterns. The MATLAB code developed for this study enhanced the accuracy of pollutant concentration predictions and provided capabilities for visualizing pollutant dispersion in 3D plots. Furthermore, the code could predict the maximum concentration of pollutants at ground level, eliminating the need to use the Ranchoux model for predictions. Complementing the modelling approach, empirical soil sampling and analysis were conducted to evaluate heavy metal concentrations in the vicinity of the facility. This integrated methodology underscored the importance of computational modelling in air pollution assessment and highlighted the necessity of soil analysis to obtain a holistic understanding of environmental impacts. The findings emphasized the effectiveness of current emissions controls while advocating for ongoing monitoring to safeguard public health and environmental integrity.

Keywords: air emissions, Gaussian Plume Model, MATLAB, soil contamination, air pollution monitoring, waste-to-energy, pollutant dispersion visualization, heavy metal analysis, environmental impact assessment, emission control effectiveness

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Authors: Zohreh Soleimani, Sally Salome Shahzad, Stamatis Zoras

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As a result of current energy and environmental issues, the human body is known as one of the promising candidate for converting wasted heat to electricity (Seebeck effect). Thermoelectric generator (TEG) is one of the most prevalent means of harvesting body heat and converting that to eco-friendly electrical power. However, the uneven distribution of the body heat and its curvature geometry restrict harvesting adequate amount of energy. To perfectly transform the heat radiated by the body into power, the most direct solution is conforming the thermoelectric generators (TEG) with the arbitrary surface of the body and increase the temperature difference across the thermoelectric legs. Due to this, a computational survey through COMSOL Multiphysics is presented in this paper with the main focus on the impact of integrating a flexible wearable TEG with a corrugated shaped heat sink on the module power output. To eliminate external parameters (temperature, air flow, humidity), the simulations are conducted within indoor thermal level and when the wearer is stationary. The full thermoelectric characterization of the proposed TEG fabricated by a wavy shape heat sink has been computed leading to a maximum power output of 25µW/cm2 at a temperature gradient nearly 13°C. It is noteworthy that for the flexibility of the proposed TEG and heat sink, the applicability and efficiency of the module stay high even on the curved surfaces of the body. As a consequence, the results demonstrate the superiority of such a TEG to the most state of the art counterparts fabricated with no heat sink and offer a new train of thought for the development of self-sustained and unobtrusive wearable power suppliers which generate energy from low grade dissipated heat from the body.

Keywords: device simulation, flexible thermoelectric module, heat sink, human body heat

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Authors: Mohsin Raza, Rizwan Latif, Syed Adnan Qasim, Imran Shafi

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High torque low-speed diesel engine has a wide range of industrial and commercial applications. In literature, it’s found that lot of work has been done for the high-speed diesel engine and research on High Torque low-speed is rare. The fuel injection plays a key role in the efficiency of engine and reduction in exhaust emission. The fuel breakup plays a critical role in air-fuel mixture and spray combustion. The current study explains numerically an important phenomenon in spray combustion which is deformation and breakup of liquid drops in compression ignition internal combustion engine. The secondary breakup and its influence on spray and characteristics of compressed gas in-cylinder have been calculated by using simulation software in the backdrop of high torque low-speed diesel like conditions. The secondary spray breakup is modeled with KH - RT instabilities. The continuous field is described by turbulence model and dynamics of the dispersed droplet is modeled by Lagrangian tracking scheme. The results by using KH - RT model are compared against other default methods in OpenFOAM and published experimental data from research and implemented in CFD (Computational Fluid Dynamics). These numerical simulation, done in OpenFoam and Matlab, results are analyzed for the complete 720- degree 4 stroke engine cycle at a low engine speed, for favorable agreement to be achieved. Results thus obtained will be analyzed for better evaporation in near nozzle region. The proposed analyses will further help in better engine efficiency, low emission and improved fuel economy.

Keywords: diesel fuel, KH-RT, Lagrangian , Open FOAM, secondary breakup

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Authors: Kenneth D. Aiello, M. Simeone, Manfred Laubichler

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It is increasingly difficult to analyze emerging scientific fields as contemporary scientific fields are more dynamic, their boundaries are more porous, and the relational possibilities have increased due to Big Data and new information sources. In biomedicine, where funding, medical categories, and medical jurisdiction are determined by distinct boundaries on biomedical research fields and definitions of concepts, ambiguity persists between the microbiome, metabolome, and metagenome research fields. This ambiguity continues despite efforts by institutions and organizations to establish parameters on the core concepts and research discourses. Further, the explosive growth of microbiome, metabolome, and metagenomic research has led to unknown variation and covariation making application of findings across subfields or coming to a consensus difficult. This study explores the evolution and variation of knowledge within the microbiome, metabolome, and metagenome research fields related to ambiguous scholarly language and commensurable theoretical frameworks via a semantic analysis of key concepts and narratives. A computational historical framework of cultural evolution and large-scale publication data highlight the boundaries and overlaps between the competing scientific discourses surrounding the three research areas. The results of this study highlight how discourse and language distribute power within scholarly and scientific networks, specifically the power to set and define norms, central questions, methods, and knowledge.

Keywords: biomedicine, conceptual change, history of science, philosophy of science, science of science, sociolinguistics, sociology of knowledge

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Rabih Ghostine, Craig Kapfer, Viswanathan Kannan, Ibrahim Hoteit

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Urban flooding resulting from a sudden release of water due to dam-break or excessive rainfall is a serious threatening environment hazard, which causes loss of human life and large economic losses. Anticipating floods before they occur could minimize human and economic losses through the implementation of appropriate protection, provision, and rescue plans. This work reports on the numerical modelling of flash flood propagation in urban areas after an excessive rainfall event or dam-break. A two-dimensional (2D) depth-averaged shallow water model is used with a refined unstructured grid of triangles for representing the urban area topography. The 2D shallow water equations are solved using a second-order well-balanced discontinuous Galerkin scheme. Theoretical test case and three flood events are described to demonstrate the potential benefits of the scheme: (i) wetting and drying in a parabolic basin (ii) flash flood over a physical model of the urbanized Toce River valley in Italy; (iii) wave propagation on the Reyran river valley in consequence of the Malpasset dam-break in 1959 (France); and (iv) dam-break flood in October 1982 at the town of Sumacarcel (Spain). The capability of the scheme is also verified against alternative models. Computational results compare well with recorded data and show that the scheme is at least as efficient as comparable second-order finite volume schemes, with notable efficiency speedup due to parallelization.

Keywords: dam-break, discontinuous Galerkin scheme, flood modeling, shallow water equations

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Authors: Guermache Ahmed Chafik, Khelfellah Ismail, Ait-Ali Takfarines

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The Savonius vertical axis wind turbine is characterized by sufficient starting torque at low wind speeds, simple design and does not require orientation to the wind direction; however, the developed power is lower than other types of wind turbines such as Darrieus. To increase these performances several studies and researches have been developed, such as optimizing blades shape, using passive controls and also minimizing power losses sources like the resisting torque due to friction. This work aims to estimate the performance of a Savonius wind turbine introducing a User Defined Function to the CFD model analyzing resisting torque. This User Defined Function is developed to simulate the action of the wind speed on the rotor; it receives the moment coefficient as an input to compute the rotational velocity that should be imposed on computational domain rotating regions. The rotational velocity depends on the aerodynamic moment applied on the turbine and the resisting torque, which is considered a linear function. Linking the implemented User Defined Function with the CFD solver allows simulating the real functioning of the Savonius turbine exposed to wind. It is noticed that the wind turbine takes a while to reach the stationary regime where the rotational velocity becomes invariable; at that moment, the tip speed ratio, the moment and power coefficients are computed. To validate this approach, the power coefficient versus tip speed ratio curve is compared with the experimental one. The obtained results are in agreement with the available experimental results.

Keywords: resistant torque modeling, Savonius wind turbine, user-defined function, vertical axis wind turbine performances

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Authors: Haripriya Chakraborty

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The healthcare system in the United States is considered to be one of the most inefficient and expensive systems when compared to other developed countries. Consequently, there are persistent concerns regarding the overall functioning of this system. For instance, the large number of uninsured individuals and high premiums are pressing issues that are shown to have a negative effect on health outcomes with possible life-threatening consequences. The Affordable Care Act (ACA), which was signed into law in 2010, was aimed at improving some of these inefficiencies. This paper aims at providing a computational mechanism to examine some of these inefficiencies and the effects that policy proposals may have on reducing these inefficiencies. Agent-based modeling is an invaluable tool that provides a flexible framework to model complex systems. It can provide an important perspective into the nature of some interactions that occur and how the benefits of these interactions are allocated. In this paper, we propose a novel and versatile agent-based model with realistic assumptions to simulate the dynamics of a health insurance marketplace that contains a mixture of private and public insurers and individuals. We use this model to analyze the characteristics, motivations, payoffs, and strategies of these agents. In addition, we examine the effects of certain policies, including some of the provisions of the ACA, aimed at reducing the uninsured rate and the cost of premiums to move closer to a system that is more equitable and improves health outcomes for the general population. Our test results confirm the usefulness of our agent-based model in studying this complicated issue and suggest some implications for public policies aimed at healthcare reform.

Keywords: agent-based modeling, healthcare reform, insurance markets, public policy

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Authors: Yuxi Zhu, Zhenqian Chen

Abstract:

It is of great interest to investigate the thermal properties of non-precious metal catalysts for Proton exchange membrane fuel cell (PEMFC) based on the thermal management requirements. Due to the low symmetry of materials, to accurately obtain the thermal conductivity of materials, it is necessary to obtain the second and third order force constants by combining density functional theory and machine learning interatomic potential. To be specific, the interatomic force constants are obtained by moment tensor potential (MTP), which is trained by the computational trajectory of Ab initio molecular dynamics (AIMD) at 50, 300, 600, and 900 K for 1 ps each, with a time step of 1 fs in the AIMD computation. And then the thermal conductivity can be obtained by solving the Boltzmann transport equation. In this paper, the thermal transport properties of single metal atom catalysts are studied for the first time to our best knowledge by machine-learning interatomic potential (MLIP). Results show that the single metal atom catalysts exhibit anisotropic thermal conductivities and partially exhibit good thermal conductivity. The average lattice thermal conductivities of G-FeN₄, G-CoN₄ and G-NiN₄ at 300 K are 88.61 W/mK, 205.32 W/mK and 210.57 W/mK, respectively. While other single metal atom catalysts show low thermal conductivity due to their low phonon lifetime. The results also show that low-frequency phonons (0-10 THz) dominate thermal transport properties. The results provide theoretical insights into the application of single metal atom catalysts in thermal management.

Keywords: proton exchange membrane fuel cell, single metal atom catalysts, density functional theory, thermal conductivity, machine-learning interatomic potential

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Authors: Hiba El Assibi

Abstract:

This study aims to weakly solve Kalah, a two-player board game, by developing a start-to-finish winning strategy using an optimized Minimax algorithm with Alpha-Beta Pruning. In weakly solving Kalah, our focus is on creating an optimal strategy from the game's beginning rather than analyzing every possible position. The project will explore additional enhancements like symmetry checking and code optimizations to speed up the decision-making process. This approach is expected to give insights into efficient strategy formulation in board games and potentially help create games with a fair distribution of outcomes. Furthermore, this research provides a unique perspective on human versus Artificial Intelligence decision-making in strategic games. By comparing the AI-generated optimal moves with human choices, we can explore how seemingly advantageous moves can, in the long run, be harmful, thereby offering a deeper understanding of strategic thinking and foresight in games. Moreover, this paper discusses the evaluation of our strategy against existing methods, providing insights on performance and computational efficiency. We also discuss the scalability of our approach to the game, considering different board sizes (number of pits and stones) and rules (different variations) and studying how that affects performance and complexity. The findings have potential implications for the development of AI applications in strategic game planning, enhancing our understanding of human cognitive processes in game settings, and offer insights into creating balanced and engaging game experiences.

Keywords: minimax, alpha beta pruning, transposition tables, weakly solving, game theory

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Authors: Karthikeyan Kalirajan, Ashok Joshi

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An optimization problem is to setup to maximize the terminal kinetic energy of a maneuverable reentry vehicle (MaRV). The target location, the impact angle is given as constraints. The MaRV uses an explicit guidance law called Vector guidance. This law has two gains which are taken as decision variables. The problem is to find the optimal value of these gains which will result in minimum miss distance and impact angle error. Using a simple 3DOF non-rotating flat earth model and Lockheed martin HP-MARV as the reentry vehicle, the nature of solutions of the optimization problem is studied. This is achieved by carrying out a parametric study for a range of closed loop gain values and the corresponding impact angle error and the miss distance values are generated. The results show that there are well defined lower and upper bounds on the gains that result in near optimal terminal guidance solution. It is found from this study, that there exist common permissible regions (values of gains) where all constraints are met. Moreover, the permissible region lies between flat regions and hence the optimization algorithm has to be chosen carefully. It is also found that, only one of the gain values is independent and that the other dependent gain value is related through a simple straight-line expression. Moreover, to reduce the computational burden of finding the optimal value of two gains, a guidance law called Diveline guidance is discussed, which uses single gain. The derivation of the Diveline guidance law from Vector guidance law is discussed in this paper.

Keywords: Marv guidance, reentry trajectory, trajectory optimization, guidance gain selection

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Authors: David Ryan, Martin Breugst, Turlough Downes, Peter A. Byrne, Gerard P. McGlacken

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An ambident nucleophile is a nucleophile that possesses two or more distinct nucleophilic sites that are linked through resonance and are effectively “in competition” for reaction with an electrophile. Examples include enolates, pyridone anions, and nitrite anions, among many others. Reactions of ambident nucleophiles and electrophiles are extremely prevalent at all levels of organic synthesis. The principle of hard and soft acids and bases (the “HSAB principle”) is most commonly cited in the explanation of selectivities in such reactions. Although this rationale is pervasive in any discussion on ambident reactivity, the HSAB principle has received considerable criticism. As a result, the principle’s supplantation has become an area of active interest in recent years. This project focuses on developing a model for rationalizing ambident reactivity. Presented here is an approach that incorporates computational calculations and experimental kinetic data to construct Gibbs energy profile diagrams. The preferred site of alkylation of nitrite anion with a range of ‘hard’ and ‘soft’ alkylating agents was established by ¹H NMR spectroscopy. Pseudo-first-order rate constants were measured directly by ¹H NMR reaction monitoring, and the corresponding second-order constants and Gibbs energies of activation were derived. These, in combination with computationally derived standard Gibbs energies of reaction, were sufficient to construct Gibbs energy wells. By representing the ambident system as a series of overlapping Gibbs energy wells, a more intuitive picture of ambident reactivity emerges. Here, previously unexplained switches in reactivity in reactions involving closely related electrophiles are elucidated.

Keywords: ambident, Gibbs, nucleophile, rates

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Authors: Sani M. Lawal, Idris Musa, Aliyu D. Usman

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The Pareto approach of optimal solutions in a search space that evolved in multi-objective optimization problems is adopted in this paper, which stands for a set of solutions in the search space. This paper aims at presenting an optimal placement of Distributed Generation (DG) in radial distribution networks with an optimal size for minimization of power loss and voltage deviation as well as maximizing voltage profile of the networks. And these problems are formulated using particle swarm optimization (PSO) as a constraint nonlinear optimization problem with both locations and sizes of DG being continuous. The objective functions adopted are the total active power loss function and voltage deviation function. The multiple nature of the problem, made it necessary to form a multi-objective function in search of the solution that consists of both the DG location and size. The proposed PSO algorithm is used to determine optimal placement and size of DG in a distribution network. The output indicates that PSO algorithm technique shows an edge over other types of search methods due to its effectiveness and computational efficiency. The proposed method is tested on the standard IEEE 34-bus and validated with 33-bus test systems distribution networks. Results indicate that the sizing and location of DG are system dependent and should be optimally selected before installing the distributed generators in the system and also an improvement in the voltage profile and power loss reduction have been achieved.

Keywords: distributed generation, pareto, particle swarm optimization, power loss, voltage deviation

Procedia PDF Downloads 364