Search results for: dimensional affect prediction

Authors: Bassim Shaheen Bachy, Jörg Franke

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In this study, a three dimensional numerical heat transfer model has been used to simulate the laser structuring of polymer substrate material in the Three-Dimensional Molded Interconnect Device (3D MID) which is used in the advanced multi-functional applications. A finite element method (FEM) transient thermal analysis is performed using APDL (ANSYS Parametric Design Language) provided by ANSYS. In this model, the effect of surface heat source was modeled with Gaussian distribution, also the effect of the mixed boundary conditions which consist of convection and radiation heat transfers have been considered in this analysis. The model provides a full description of the temperature distribution, as well as calculates the depth and the width of the groove upon material removal at different set of laser parameters such as laser power and laser speed. This study also includes the experimental procedure to study the effect of laser parameters on the depth and width of the removal groove metal as verification to the modeled results. Good agreement between the experimental and the model results is achieved for a wide range of laser powers. It is found that the quality of the laser structure process is affected by the laser scan speed and laser power. For a high laser structured quality, it is suggested to use laser with high speed and moderate to high laser power.

Keywords: laser structuring, simulation, finite element analysis, thermal modeling

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Authors: Sulemana Ibrahim

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Food security remains a paramount global challenge, with vulnerable regions grappling with issues of hunger and malnutrition. This study embarks on a comprehensive exploration of data-driven strategies aimed at ameliorating food security in such regions. Our research employs a multifaceted approach, integrating data analytics to predict crop yields, optimizing supply chains, and enhancing food distribution networks. The study unfolds as a multi-dimensional analysis, commencing with the development of robust machine learning models harnessing remote sensing data, historical crop yield records, and meteorological data to foresee crop yields. These predictive models, underpinned by convolutional and recurrent neural networks, furnish critical insights into anticipated harvests, empowering proactive measures to confront food insecurity. Subsequently, the research scrutinizes supply chain optimization to address food security challenges, capitalizing on linear programming and network optimization techniques. These strategies intend to mitigate loss and wastage while streamlining the distribution of agricultural produce from field to fork. In conjunction, the study investigates food distribution networks with a particular focus on network efficiency, accessibility, and equitable food resource allocation. Network analysis tools, complemented by data-driven simulation methodologies, unveil opportunities for augmenting the efficacy of these critical lifelines. This study also considers the ethical implications and privacy concerns associated with the extensive use of data in the realm of food security. The proposed methodology outlines guidelines for responsible data acquisition, storage, and usage. The ultimate aspiration of this research is to forge a nexus between data science and food security policy, bestowing actionable insights to mitigate the ordeal of food insecurity. The holistic approach converging data-driven crop yield forecasts, optimized supply chains, and improved distribution networks aspire to revitalize food security in the most vulnerable regions, elevating the quality of life for millions worldwide.

Keywords: data-driven strategies, crop yield prediction, supply chain optimization, food distribution networks

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Authors: Mohammed T. Hayajneh

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Machining of Metal Matrix Composites (MMCs) is very significant process and has been a main problem that draws many researchers to investigate the characteristics of MMCs during different machining process. The poor machining properties of hard particles reinforced MMCs make drilling process a rather interesting task. Unlike drilling of conventional materials, many problems can be seriously encountered during drilling of MMCs, such as tool wear and cutting forces. Cutting tool wear is a very significant concern in industries. Cutting tool wear not only influences the quality of the drilled hole, but also affects the cutting tool life. Prediction the cutting tool life during drilling is essential for optimizing the cutting conditions. However, the relationship between tool life and cutting conditions, tool geometrical factors and workpiece material properties has not yet been established by any machining theory. In this research work, fuzzy subtractive clustering system has been used to model the cutting tool life in drilling of Al₂O₃ particle reinforced aluminum alloy composite to investigate of the effect of cutting conditions on cutting tool life. This investigation can help in controlling and optimizing of cutting conditions when the process parameters are adjusted. The built model for prediction the tool life is identified by using drill diameter, cutting speed, and cutting feed rate as input data. The validity of the model was confirmed by the examinations under various cutting conditions. Experimental results have shown the efficiency of the model to predict cutting tool life.

Keywords: composite, fuzzy, tool life, wear

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: R. Burahee, B. Chassinat, T. de Laclos, A. Dépée, A. Sastim

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The purpose of this paper is to propose a solution to detect, predict and reconstitute rain drops in real time – during the night – using an embedded material with an infrared camera. To prevent the system from needing too high hardware resources, simple models are considered in a powerful image treatment algorithm reducing considerably calculation time in OpenCV software. Using a smart model – drops will be matched thanks to a process running through two consecutive pictures for implementing a sophisticated tracking system. With this system drops computed trajectory gives information for predicting their future location. Thanks to this technique, treatment part can be reduced. The hardware system composed by a Raspberry Pi is optimized to host efficiently this code for real time execution.

Keywords: reconstitution, prediction, detection, rain drop, real time, raspberry, infrared

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Authors: J. K. Alhassan, B. Attah, S. Misra

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Human beings have the ability to make logical decisions. Although human decision - making is often optimal, it is insufficient when huge amount of data is to be classified. medical dataset is a vital ingredient used in predicting patients health condition. In other to have the best prediction, there calls for most suitable machine learning algorithms. This work compared the performance of Artificial Neural Network (ANN) and Decision Tree Algorithms (DTA) as regards to some performance metrics using diabetes data. The evaluations was done using weka software and found out that DTA performed better than ANN. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were the two algorithms used for ANN, while RegTree and LADTree algorithms were the DTA models used. The Root Mean Squared Error (RMSE) of MLP is 0.3913,that of RBF is 0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206 respectively.

Keywords: artificial neural network, classification, decision tree algorithms, diabetes mellitus

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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The Greek Energy Market is structured as a mandatory pool where the producers make their bid offers in day-ahead basis. The System Operator solves an optimization routine aiming at the minimization of the cost of produced electricity. The solution of the optimization problem leads to the calculation of the System Marginal Price (SMP). Accurate forecasts of the SMP can lead to increased profits and more efficient portfolio management from the producer`s perspective. Aim of this study is to provide a comparative analysis of various machine learning models such as artificial neural networks and neuro-fuzzy models for the prediction of the SMP of the Greek market. Machine learning algorithms are favored in predictions problems since they can capture and simulate the volatilities of complex time series.

Keywords: deregulated energy market, forecasting, machine learning, system marginal price

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Authors: P. R. Maiti, S. K. Bhattacharyya

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Sloshing is a physical phenomenon characterized by the oscillation of unrestrained free surface of liquid in a partially liquid filled container subjected to external excitation. Determination of sloshing frequency in container is important to avoid resonance condition of the system. The complex behavior of the free surface movement and its combined mode of vibration make difficulty for exact analysis of sloshing. In the present study, numerical analysis is carried out for a partially liquid filled tank under external forces. Boundary element approach is used to formulate the sloshing problem in two -dimensional prismatic container with potential flow. Effort has been made to find slosh response for two dimensional problems in partially liquid filled prismatic container.

Keywords: sloshing, boundary element method, prismatic container, oscillation

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Authors: Vijay Kumar Kukreja, Ravneet Kaur

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In this paper, one dimensional advection diffusion model is analyzed using finite difference method based on Crank-Nicolson scheme. A practical problem of filter cake washing of chemical engineering is analyzed. The model is converted into dimensionless form. For the grid Ω × ω = [0, 1] × [0, T], the Crank-Nicolson spatial derivative scheme is used in space domain and forward difference scheme is used in time domain. The scheme is found to be unconditionally convergent, stable, first order accurate in time and second order accurate in space domain. For a test problem, numerical results are compared with the analytical ones for different values of parameter.

Keywords: Crank-Nicolson scheme, Lax-Richtmyer theorem, stability, consistency, Peclet number, Greschgorin circle

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Authors: Venencia Albert, Arulselvi Subramanian, Hara Prasad Pati, Asok K. Mukhophadhyay

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Acute traumatic coagulopathy in an endogenous dysregulation of the intrinsic coagulation system in response to the injury, associated with three-fold risk of poor outcome, and is more amenable to corrective interventions, subsequent to early identification and management. Multiple definitions for stratification of the patients' risk for early acute coagulopathy have been proposed, with considerable variations in the defining criteria, including several trauma-scoring systems based on prehospital data. We aimed to develop a clinically relevant definition for acute coagulopathy of trauma based on conventional coagulation assays and to assess its efficacy in comparison to recently established prehospital prediction models. Methodology: Retrospective data of all trauma patients (n = 490) presented to our level I trauma center, in 2014, was extracted. Receiver operating characteristic curve analysis was done to establish cut-offs for conventional coagulation assays for identification of patients with acute traumatic coagulopathy was done. Prospectively data of (n = 100) adult trauma patients was collected and cohort was stratified by the established definition and classified as "coagulopathic" or "non-coagulopathic" and correlated with the Prediction of acute coagulopathy of trauma score and Trauma-Induced Coagulopathy Clinical Score for identifying trauma coagulopathy and subsequent risk for mortality. Results: Data of 490 trauma patients (average age 31.85±9.04; 86.7% males) was extracted. 53.3% had head injury, 26.6% had fractures, 7.5% had chest and abdominal injury. Acute traumatic coagulopathy was defined as international normalized ratio ≥ 1.19; prothrombin time ≥ 15.5 s; activated partial thromboplastin time ≥ 29 s. Of the 100 adult trauma patients (average age 36.5±14.2; 94% males), 63% had early coagulopathy based on our conventional coagulation assay definition. Overall prediction of acute coagulopathy of trauma score was 118.7±58.5 and trauma-induced coagulopathy clinical score was 3(0-8). Both the scores were higher in coagulopathic than non-coagulopathic patients (prediction of acute coagulopathy of trauma score 123.2±8.3 vs. 110.9±6.8, p-value = 0.31; trauma-induced coagulopathy clinical score 4(3-8) vs. 3(0-8), p-value = 0.89), but not statistically significant. Overall mortality was 41%. Mortality rate was significantly higher in coagulopathic than non-coagulopathic patients (75.5% vs. 54.2%, p-value = 0.04). High prediction of acute coagulopathy of trauma score also significantly associated with mortality (134.2±9.95 vs. 107.8±6.82, p-value = 0.02), whereas trauma-induced coagulopathy clinical score did not vary be survivors and non-survivors. Conclusion: Early coagulopathy was seen in 63% of trauma patients, which was significantly associated with mortality. Acute traumatic coagulopathy defined by conventional coagulation assays (international normalized ratio ≥ 1.19; prothrombin time ≥ 15.5 s; activated partial thromboplastin time ≥ 29 s) demonstrated good ability to identify coagulopathy and subsequent mortality, in comparison to the prehospital parameter-based scoring systems. Prediction of acute coagulopathy of trauma score may be more suited for predicting mortality rather than early coagulopathy. In emergency trauma situations, where immediate corrective measures need to be taken, complex multivariable scoring algorithms may cause delay, whereas coagulation parameters and conventional coagulation tests will give highly specific results.

Keywords: trauma, coagulopathy, prediction, model

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Authors: Wade Ghribi, Abdelmoty M. Ahmed, Ahmed Said Badawy, Belgacem Bouallegue

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In higher education institutions, the most pressing priority is to improve student performance and retention. Large volumes of student data are used in Educational Data Mining techniques to find new hidden information from students' learning behavior, particularly to uncover the early symptom of at-risk pupils. On the other hand, data with noise, outliers, and irrelevant information may provide incorrect conclusions. By identifying features of students' data that have the potential to improve performance prediction results, comparing and identifying the most appropriate ensemble learning technique after preprocessing the data, and optimizing the hyperparameters, this paper aims to develop a reliable students' performance prediction model for Higher Education Institutions. Data was gathered from two different systems: a student information system and an e-learning system for undergraduate students in the College of Computer Science of a Saudi Arabian State University. The cases of 4413 students were used in this article. The process includes data collection, data integration, data preprocessing (such as cleaning, normalization, and transformation), feature selection, pattern extraction, and, finally, model optimization and assessment. Random Forest, Bagging, Stacking, Majority Vote, and two types of Boosting techniques, AdaBoost and XGBoost, are ensemble learning approaches, whereas Decision Tree, Support Vector Machine, and Artificial Neural Network are supervised learning techniques. Hyperparameters for ensemble learning systems will be fine-tuned to provide enhanced performance and optimal output. The findings imply that combining features of students' behavior from e-learning and students' information systems using Majority Vote produced better outcomes than the other ensemble techniques.

Keywords: educational data mining, student performance prediction, e-learning, classification, ensemble learning, higher education

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Authors: Kok Hong Tan, Hing Wah Lee, Jhih-Wei Chen, Chang Fu Dee, Chung-Lin Wu, Siang-Piao Chai, Wei Sea Chang

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Semiconductor photocatalyst is known as one of the key roles in developing clean and sustainable energy. However, most of the semiconductor only possesses photoactivity within the UV light region, and hence, decreases the overall photocatalyst efficiency. Generally, the overall effectiveness of the photocatalyst activity is determined by three critical steps: (i) light absorption efficiency and photoexcitation electron-hole pair generation, (ii) separation and migration of charge carriers to the surface of the photocatalyst, and (iii) surface reaction of the carriers with its environment. Much effort has been invested on optimizing hierarchical nanostructures of semiconductors for efficient photoactivity due to the fact that the visible light absorption capability and occurrence of the chemical reactions mostly depend on the dimension of photocatalysts. In this work, we incorporated zero-dimensional (0D) gold nanoparticles (AuNPs) and one dimensional (1D) Zinc Oxide (ZnO) nanorods (NRs) onto strontium titanate (STO) for efficient visible light absorption, charge transfer, and separation. We demonstrate that the electrical and optical properties of the photocatalyst can be tuned by controlling the dimensional structures of AuNPs and ZnO NRs. We found that smaller AuNPs sizes exhibited higher photoactivity because of Fermi level shifting toward the conductive band of STO, STO band gap narrowing and broadening of absorption spectrum to the visible light region. For ZnO NRs, it was found that the average ZnO NRs c-axis length must achieve of certain length to induce multiphoton absorption as a result of light reflection and trapping behavior in the free space between adjacent ZnO NRs hence broadening the absorption spectrum of ZnO from UV to visible light region. This work opens up a new way of broadening the absorption spectrum by incorporating controllable nanostructures of semiconductors, which is important in optimizing the solar water splitting process.

Keywords: gold nanoparticles, photoelectrochemical, PEC, semiconductor photocatalyst, zinc oxide nanorods

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to preventing deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network.

Keywords: accident risks estimation, artificial neural network, deep learning, k-mean, road safety

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Authors: Yilin Liao, Hewen Li, Paula McConvey

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Speed skaters often face a risk of concussion when they fall on the ice floor and impact crash mats during practices and competitive races. Several variables, including those related to the skater, the crash mat, and the impact position (body side/head/feet impact), are believed to influence the severity of the skater's concussion. While computer simulation modeling can be employed to analyze these accidents, the simulation process is time-consuming and does not provide rapid information for coaches and teams to assess the skater's injury risk in competitive events. This research paper promotes the exploration of the feasibility of using AI techniques for evaluating skater’s potential concussion severity, and to develop a fast concussion prediction tool using artificial neural networks to reduce the risk of treatment delays for injured skaters. The primary data is collected through virtual tests and physical experiments designed to simulate skater-mat impact. It is then analyzed to identify patterns and correlations; finally, it is used to train and fine-tune the artificial neural networks for accurate prediction. The development of the prediction tool by employing machine learning strategies contributes to the application of AI methods in sports science and has theoretical involvements for using AI techniques in predicting and preventing sports-related injuries.

Keywords: artificial neural networks, concussion, machine learning, impact, speed skater

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Authors: S. Habte, K. Ouazzane, P. Patel, S. Patel

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The dynamic and highly competitive nature of the consumer electronics retail industry means that businesses in this industry are experiencing different decision making challenges in relation to pricing, inventory control, consumer satisfaction and product offerings. To overcome the challenges facing retailers and create opportunities, we propose a generic data warehousing solution which can be applied to a wide range of consumer electronics retailers with a minimum configuration. The solution includes a dimensional data model, a template SQL script, a high level architectural descriptions, ETL tool developed using C#, a set of APIs, and data access tools. It has been successfully applied by ASK Outlets Ltd UK resulting in improved productivity and enhanced sales growth.

Keywords: consumer electronics, data warehousing, dimensional data model, generic, retail industry

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Authors: Hadi Kafil, Ali Ecder

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Heat transfer by natural convection in two-dimensional and three-dimensional axisymmetric enclosure fitted with partially heated vertical walls is investigated numerically. The range of Rayleigh number is varied from 10³ until convective flow becomes unstable. This research focuses on multiplicity and transition phenomena in natural convection and is based on a parametric analysis to study the onset of bifurcations. It is found that, even at low Rayleigh numbers, the flow undergoes a series of turning-point bifurcations which increase the rate of natural convention. On the other hand, by partially heating or cooling the walls, more effective results can be achieved for both heating and cooling applications, such as cooling of electronic devices and heating processes in solidification and crystal growth.

Keywords: natural convection, partial heated, onset of bifurcation, Rayleigh number

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Authors: Rajeev Kumar, Angad Singh Kushwaha, Amritanshu Pandey, S. K. Srivastava

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Photonic crystals (PCs) have attracted much attention due to its electromagnetic properties and potential applications. In PCs, there is certain range of wavelength where electromagnetic waves are not allowed to pass are called photonic band gap (PBG). A localized defect mode will appear within PBG, due to change in the interference behavior of light, when we create a defect in the periodic structure. We can also create different types of defect structures by inserting or removing a layer from the periodic layered structure in two and three-dimensional PCs. We can design microcavity, waveguide, and perfect mirror by creating a point defect, line defect, and palanar defect in two and three- dimensional PC structure. One-dimensional and two-dimensional PCs with defects were reported theoretically and experimentally by Smith et al.. in conventional photonic band gap structure. In the present paper, we have presented the defect mode tunability in tilted non-graded photonic crystal (NGPC) and linearly graded photonic crystal (LGPC) using lead sulphide (PbS) and titanium dioxide (TiO2) in the infrared region. A birefringent defect layer is created in NGPC and LGPC using potassium titany phosphate (KTP). With the help of transfer matrix method, the transmission properties of proposed structure is investigated for transverse electric (TE) and transverse magnetic (TM) polarization. NGPC and LGPC without defect layer is also investigated. We have found that a photonic band gap (PBG) arises in the infrared region. An additional defect layer of KTP is created in NGPC and LGPC structure. We have seen that an additional transmission mode appers in PBG region. It is due to the addition of defect layer. We have also seen the effect, linear gradation in thickness, angle of incidence, tilt angle, and thickness of defect layer, on PBG and additional transmission mode. We have observed that the additional transmission mode and PBG can be tuned by changing the above parameters. The proposed structure may be used as channeled filter, optical switches, monochromator, and broadband optical reflector.

Keywords: defect modes, graded photonic crystal, photonic crystal, tilt angle

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Authors: Mohieddine Benghersallah, Lakhdar Boulanouar, Salim Belhadi

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Currently, higher production rate with required quality and low cost is the basic principle in the competitive manufacturing industry. This is mainly achieved by using high cutting speed and feed rates. Elevated temperatures in the cutting zone under these conditions shorten tool life and adversely affect the dimensional accuracy and surface integrity of component. Thus it is necessary to find optimum cutting conditions (cutting speed, feed rate, machining environment, tool material and geometry) that can produce components in accordance with the project and having a relatively high production rate. Response surface methodology is a collection of mathematical and statistical techniques that are useful for modelling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response. The work presented in this paper examines the effects of cutting parameters (cutting speed, feed rate and depth of cut) on to the surface roughness through the mathematical model developed by using the data gathered from a series of milling experiments performed.

Keywords: Statistical analysis (RSM), Bearing steel, Coating inserts, Tool life, Surface Roughness, End milling.

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Authors: Abdullahi Jibrin, Aishetu Abdulkadir

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The development of allometric models is crucial to accurate forest biomass/carbon stock assessment. The aim of this study was to develop a set of biomass prediction models that will enable the determination of total tree aboveground biomass for savannah woodland area in Niger State, Nigeria. Based on the data collected through biometric measurements of 1816 trees and destructive sampling of 36 trees, five species specific and one site specific models were developed. The sample size was distributed equally between the five most dominant species in the study site (Vitellaria paradoxa, Irvingia gabonensis, Parkia biglobosa, Anogeissus leiocarpus, Pterocarpus erinaceous). Firstly, the equations were developed for five individual species. Secondly these five species were mixed and were used to develop an allometric equation of mixed species. Overall, there was a strong positive relationship between total tree biomass and the stem diameter. The coefficient of determination (R2 values) ranging from 0.93 to 0.99 P < 0.001 were realised for the models; with considerable low standard error of the estimates (SEE) which confirms that the total tree above ground biomass has a significant relationship with the dbh. The F-test value for the biomass prediction models were also significant at p < 0.001 which indicates that the biomass prediction models are valid. This study recommends that for improved biomass estimates in the study site, the site specific biomass models should preferably be used instead of using generic models.

Keywords: allometriy, biomass, carbon stock , model, regression equation, woodland, inventory

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Authors: Hamad Alhajeri, Abdulrahman Almutairi, A. H. Alenezi, M. H. Alhajeri, Ayedh Alajmi

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The flow around three porous cylinders in inline arrangement is investigated in this paper computationally using the commercial code FLUENT. The arrangement generally operates with the dirty gases passing through the porous cylinders, the particulate material being deposited on the outside of the cylinders. However, in a combined cycle power plant, filtration is required to allow the hot exhaust gases to be fed to a turbine without causing any physical damage to the turbine blades. Three cylinder elements are placed in a two-dimensional rectangle duct with fixed face velocity and varying the velocity ratio between the approach and face velocity. Particle trajectories are obtained for a number of particle diameters and different inlet (approach) velocity to face filtration velocity ratios to investigate the behavior of particles around the cylinder.

Keywords: porous cylinders, CFD, fluid flow, filtration

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Authors: Raoudha Chaabane, Faouzi Askri, Sassi Ben Nasrallah

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A new numerical algorithm is developed to solve coupled convection-radiation heat transfer in a two dimensional enclosure. Radiative heat transfer in participating medium has been carried out using the control volume finite element method (CVFEM). The radiative transfer equations (RTE) are formulated for absorbing, emitting and scattering medium. The density, velocity and temperature fields are calculated using the two double population lattice Boltzmann equation (LBE). In order to test the efficiency of the developed method the Rayleigh Benard convection with and without radiative heat transfer is analyzed. The obtained results are validated against available works in literature and the proposed method is found to be efficient, accurate and numerically stable.

Keywords: participating media, LBM, CVFEM- radiation coupled with convection

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Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

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In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

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Authors: David Percy

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Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

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Authors: Srinaath Anbu Durai, Wang Zhaoxia

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Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon based tools VADER and TextBlob are used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. This paper demonstrates the real world economic applications of sentiment analysis of Twitter data.

Keywords: sentiment analysis, Covid-19, housing price prediction, tweets, social media, Singapore HDB, behavioral economics, neural networks

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Authors: D. Ramajo, S. Corzo, M. Nigro

Abstract:

A Multi-dimensional computational fluid dynamics (CFD) two-phase model was developed with the aim to simulate the in-core coolant circuit of a pressurized heavy water reactor (PHWR) of a commercial nuclear power plant (NPP). Due to the fact that this PHWR is a Reactor Pressure Vessel type (RPV), three-dimensional (3D) detailed modelling of the large reservoirs of the RPV (the upper and lower plenums and the downcomer) were coupled with an in-house finite volume one-dimensional (1D) code in order to model the 451 coolant channels housing the nuclear fuel. Regarding the 1D code, suitable empirical correlations for taking into account the in-channel distributed (friction losses) and concentrated (spacer grids, inlet and outlet throttles) pressure losses were used. A local power distribution at each one of the coolant channels was also taken into account. The heat transfer between the coolant and the surrounding moderator was accurately calculated using a two-dimensional theoretical model. The implementation of subcooled boiling and condensation models in the 1D code along with the use of functions for representing the thermal and dynamic properties of the coolant and moderator (heavy water) allow to have estimations of the in-core steam generation under nominal flow conditions for a generic fission power distribution. The in-core mass flow distribution results for steady state nominal conditions are in agreement with the expected from design, thus getting a first assessment of the coupled 1/3D model. Results for nominal condition were compared with those obtained with a previous 1/3D single-phase model getting more realistic temperature patterns, also allowing visualize low values of void fraction inside the upper plenum. It must be mentioned that the current results were obtained by imposing prescribed fission power functions from literature. Therefore, results are showed with the aim of point out the potentiality of the developed model.

Keywords: PHWR, CFD, thermo-hydraulic, two-phase flow

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Authors: Kholil Kholil, Aniwidayati

Abstract:

MDS (Multi-Dimensional Scaling) is one method that has been widely used to evaluate the use of natural resources. By using Raffish software tool, we will able to analyze sustainability level of the natural resources use. This paper will discuss the level of sustainability of the reservoir using MDS (Multi-Dimensional Scaling) based on five dimensions: (1) Ecology & Layout, (2) Economics, (3) Social & Culture, (4) Regulations & Institutional, and (5) Infrastructure and Technology. MDS analysis results show that the dimension of ecological and layout, institutional and the regulation are lack of sustainability due to the low index score of 45.76 and 42.24. While for the economic, social and culture, and infrastructure and technology dimension reach each score of 63.12, 64.42, and 68.64 (only the sufficient sustainability category). It means that the sustainability performance of Cirata Reservoir seriously threatened.

Keywords: MDS, cirata reservoir, carrying capacity, water quality, sustainable development, sedimentation, sustainability index

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Authors: Hesham A. Elkaranshawy, Amr M. Abdelrazek, Hosam M. Ezzat

Abstract:

The system of ordinary nonlinear differential equations describing sliding velocity during impact with friction for a three-dimensional rigid-multibody system is developed. No analytical solutions have been obtained before for this highly nonlinear system. Hence, a power series solution is proposed. Since the validity of this solution is limited to its convergence zone, a suitable time step is chosen and at the end of it a new series solution is constructed. For a case study, the trajectory of the sliding velocity using the proposed method is built using 6 time steps, which coincides with a Runge-Kutta solution using 38 time steps.

Keywords: impact with friction, nonlinear ordinary differential equations, power series solutions, rough collision

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Authors: Rabindranath Bag, Surjeet Singh

Abstract:

Materials in which the electrons are strongly correlated provide some of the most challenging and exciting problems in condensed matter physics today. After the discovery of high critical temperature superconductivity in layered or two-dimensional copper oxides, many physicists got attention in cuprates and it led to an upsurge of interest in the synthesis and physical properties of copper-oxide based material. The quest to understand superconducting mechanism in high-temperature cuprates, drew physicist’s attention to somewhat simpler compounds consisting of spin-chains or one-dimensional lattice of coupled spins. Low-dimensional quantum magnets are of huge contemporary interest in basic sciences as well emerging technologies such as quantum computing and quantum information theory, and heat management in microelectronic devices. Spin ladder is an example of quasi one-dimensional quantum magnets which provides a bridge between one and two dimensional materials. One of the examples of quasi one-dimensional spin-ladder compounds is Sr14Cu24O41, which exhibits a lot of interesting and exciting physical phenomena in low dimensional systems. Very recently, the ladder compound Sr14Cu24O41 was shown to exhibit long-distance quantum entanglement crucial to quantum information theory. Also, it is well known that hole-compensation in this material results in very high (metal-like) anisotropic thermal conductivity at room temperature. These observations suggest that Sr14Cu24O41 is a potential multifunctional material which invites further detailed investigations. To investigate these properties one must needs a large and high quality of single crystal. But these systems are showing incongruently melting behavior, which brings many difficulties to grow a large and quality of single crystals. Hence, we are using TSFZ (Travelling Solvent Floating Zone) method to grow the high quality of single crystals of the low dimensional magnets. Apart from this, it has unique crystal structure (alternating stacks of plane containing edge-sharing CuO2 chains, and the plane containing two-leg Cu2O3 ladder with intermediate Sr layers along the b- axis), which is also incommensurate in nature. It exhibits abundant physical phenomenon such as spin dimerization, crystallization of charge holes and charge density wave. The maximum focus of research so far involved in introducing defects on A-site (Sr). However, apart from the A-site (Sr) doping, there are only few studies in which the B-site (Cu) doping of polycrystalline Sr14Cu24O41 have been discussed and the reason behind this is the possibility of two doping sites for Cu (CuO2 chain and Cu2O3 ladder). Therefore, in our present work, the crystals (pristine and Cu-site doped) were grown by using TSFZ method by tuning the growth parameters. The Laue diffraction images, optical polarized microscopy and Scanning Electron Microscopy (SEM) images confirm the quality of the grown crystals. Here, we report the single crystal growth, magnetic and transport properties of Sr14Cu24O41 and its lightly doped variants (magnetic and non-magnetic) containing less than 1% of Co, Ni, Al and Zn impurities. Since, any real system will have some amount of weak disorder, our studies on these ladder compounds with controlled dilute disorder would be significant in the present context.

Keywords: low-dimensional quantum magnets, single crystal, spin-ladder, TSFZ technique

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Authors: Aboussalih Amira, Zarza Tahar, Fedaoui Kamel, Hammoudi Saleh

Abstract:

This work focuses on the simulation of the prediction of the behaviour of austenitic stainless steel (SS) 304L under complex loading in stress and imposed strain. The Chaboche model is a cable to describe the response of the material by the combination of two isotropic and nonlinear kinematic work hardening, the model is implemented in the ZébuLon computer code. First, we represent the evolution of the axial stress as a function of the plastic strain through hysteresis loops revealing a hardening behaviour caused by the increase in stress by stress in the direction of tension/compression. In a second step, the study of the ratcheting phenomenon takes a key place in this work by the appearance of the average stress. In addition to the solicitation of the material in the biaxial direction in traction / torsion.

Keywords: damage, 304L, Ratcheting, plastic strain

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Authors: Jon Cobb, Nasir

Abstract:

Due to higher switching frequencies, the conducted Electromagnetic interference (EMI) noise is generated in a converter. It degrades the performance of a switching converter. Therefore, it is an essential requirement to mitigate EMI noise of high performance converter. Moreover, it includes two types of emission such as common mode (CM) and differential mode (DM) noise. CM noise is due to parasitic capacitance present in a converter and DM noise is caused by switching current. However, there is dire need to understand the main cause of EMI noise. Hence, we propose a novel method to predict conducted EMI noise of different converter topologies during early stage. This paper also presents the comparison of conducted electromagnetic interference (EMI) noise due to different SMPS topologies. We also make an attempt to develop an EMI noise model for a converter which allows detailed performance analysis. The proposed method is applied to different converter, as an example, and experimental results are verified the novel prediction technique.

Keywords: EMI, electromagnetic interference, SMPS, switch-mode power supply, common mode, CM, differential mode, DM, noise

Procedia PDF Downloads 1187