Search results for: computational neural networks

Authors: Masoud Ghaemi, MohammadJafar Hedayati, Faezeh Yousefzadeh, Hoseinali Heydarzadeh

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According to the statistics of the International Committee on Large Dams, internal erosion and piping (scour) are major causes of the destruction of earth-fill dams. If such dams are constructed in narrow valleys, the valley walls will increase the arching of the dam body due to the transfer of vertical and horizontal stresses, so the occurrence of hydraulic fracturing in these embankments is more likely. Roudbar Dam in Lorestan is a clay-core pebble earth-fill dam constructed in a relatively narrow valley in western Iran. Three years after the onset of impoundment, there has been a fall in dam behavior. Evaluation of the dam behavior based on the data recorded on the instruments installed inside the dam body and foundation confirms the occurrence of internal erosion in the lower and adjacent parts of the core on the left support (abutment). The phenomenon of hydraulic fracturing is one of the main causes of the onset of internal erosion in this dam. Accordingly, the main objective of this paper is to evaluate the validity of this hypothesis. To evaluate the validity of this hypothesis, the dam behavior during construction and impoundment has been first simulated with a three-dimensional numerical model. Then, using validated empirical equations, the safety factor of the occurrence of hydraulic fracturing phenomenon upstream of the dam score was calculated. Then, using the artificial neural network, the failure time of the given section was predicted based on the maximum stress trend created. The study results show that steep slopes of valley walls, sudden changes in coefficient, and differences in compressibility properties of dam body materials have caused considerable stress transfer from core to adjacent valley walls, especially at its lower levels. This has resulted in the coefficient of confidence of the occurrence of hydraulic fracturing in each of these areas being close to one in each of the empirical equations used.

Keywords: arching, artificial neural network, FLAC3D, hydraulic fracturing, internal erosion, pore water pressure

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Authors: Anna Rinaldi, Pierfrancesco Dellino

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COVID-19 vaccine hesitancy can be observed at different rates in different countries. In June 2021, 1,068 people were surveyed in France and Italy to inquire about individual potential acceptance, focusing on time preferences in a risk-return framework: having the vaccination today, in a month, and in 3 months; perceived risks of vaccination and COVID-19; and expected benefit of the vaccine. A randomized controlled trial was conducted to understand how everyday stimuli like fact-based news about vaccines impact an audience's acceptance of vaccination. The main experiment involved two groups of participants and two different articles about vaccine-related thrombosis taken from two Italian newspapers. One article used a more abstract description and language, and the other used a more anecdotal description and concrete language; each group read only one of these articles. Two other groups were assigned categorization tasks; one was asked to complete a concrete categorization task, and the other an abstract categorization task. Individual preferences for vaccination were found to be variable and unstable over time, and individual choices of accepting, refusing, or delaying could be affected by the way news is written. In order to understand these dynamic preferences, the present work proposes a new model based on seven categories of human behaviors that were validated by a neural network. A treatment effect was observed: participants who read the articles shifted to vaccine hesitancy categories more than participants assigned to other treatments and control. Furthermore, there was a significant gender effect, showing that the type of language leading to a lower hesitancy rate for men is correlated with a higher hesitancy rate for women and vice versa. This outcome should be taken into consideration for an appropriate gender-based communication campaign aimed at achieving herd immunity. The trial was registered at ClinicalTrials.gov NCT05582564 (17/10/2022).

Keywords: vaccine hesitancy, risk elicitation, neural network, covid19

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Authors: Muhammad Abubakar Alhassan, Diane Pennington

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Social networking sites have become a space for people to discuss public health issues such as non-suicidal self-injury (NSSI). There are thousands of tweets containing self-harm and self-injury hashtags on Twitter. It is difficult to distinguish between different users who participate in self-injury discussions on Twitter and how their opinions change over time. Also, it is challenging to understand the topics surrounding NSSI discussions on Twitter. We retrieved tweets using #selfham and #selfinjury hashtags and investigated those from the United kingdom. We applied inductive coding and grouped tweeters into different categories. This study used the Latent Dirichlet Allocation (LDA) algorithm to infer the optimum number of topics that describes our corpus. Our findings revealed that many of those participating in NSSI discussions are non-professional users as opposed to medical experts and academics. Support organisations, medical teams, and academics were campaigning positively on rais-ing self-injury awareness and recovery. Using LDAvis visualisation technique, we selected the top 20 most relevant terms from each topic and interpreted the topics as; children and youth well-being, self-harm misjudgement, mental health awareness, school and mental health support and, suicide and mental-health issues. More than 50% of these topics were discussed in England compared to Scotland, Wales, Ireland and Northern Ireland. Our findings highlight the advantages of using the Twitter social network in tackling the problem of self-injury through awareness. There is a need to study the potential risks associated with the use of social networks among self-injurers.

Keywords: self-harm, non-suicidal self-injury, Twitter, social networks

Procedia PDF Downloads 131

Authors: Geetanjali Yadav, Arpit Mishra, Parthsarathi Ghosh, Ramkrishna Sen

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The world is facing problems of increasing global CO2 emissions, climate change and fuel crisis. Therefore, several renewable and sustainable energy alternatives should be investigated to replace non-renewable fuels in future. Algae presents itself a versatile feedstock for the production of variety of fuels (biodiesel, bioethanol, bio-hydrogen etc.) and high value compounds for food, fodder, cosmetics and pharmaceuticals. Microalgae are simple microorganisms that require water, light, CO2 and nutrients for growth by the process of photosynthesis and can grow in extreme environments, utilize waste gas (flue gas) and waste waters. Mixing, however, is a crucial parameter within the culture system for the uniform distribution of light, nutrients and gaseous exchange in addition to preventing settling/sedimentation, creation of dark zones etc. The overarching goal of the present study is to improve photobioreactor (PBR) design for enhancing dissolution of CO2 from ambient air (0.039%, v/v), pure CO2 and coal-fired flue gas (10 ± 2%) into microalgal PBRs. Computational fluid dynamics (CFD), a state-of-the-art technique has been used to solve partial differential equations with turbulence closure which represents the dynamics of fluid in a photobioreactor. In this paper, the hydrodynamic performance of the PBR has been characterized and compared with that of the conventional bubble column PBR using CFD. Parameters such as flow rate (Q), mean velocity (u), mean turbulent kinetic energy (TKE) were characterized for each experiment that was tested across different aeration schemes. The results showed that the modified PBR design had superior liquid circulation properties and gas-liquid transfer that resulted in creation of uniform environment inside PBR as compared to conventional bubble column PBR. The CFD technique has shown to be promising to successfully design and paves path for a future research in order to develop PBRs which can be commercially available for scale-up microalgal production.

Keywords: computational fluid dynamics, microalgae, bubble column photbioreactor, flue gas, simulation

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Authors: Jinwooung Kim, Jae-Hwan Jung, Seong-Jun Kim, Sung-Ah Kim

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Because the building envelope has a significant impact on the operation and maintenance stage of the building, designing the facade considering the performance can improve the performance of the building and lower the maintenance cost of the building. In general, however, optimizing two or more performance factors confronts the limits of time and computational tools. The optimization phase typically repeats infinitely until a series of processes that generate alternatives and analyze the generated alternatives achieve the desired performance. In particular, as complex geometry or precision increases, computational resources and time are prohibitive to find the required performance, so an optimization methodology is needed to deal with this. Instead of directly analyzing all the alternatives in the optimization process, applying experimental techniques (heuristic method) learned through experimentation and experience can reduce resource waste. This study proposes and verifies a method to optimize the double envelope of a building composed of a perforated panel using machine learning to the design geometry and quantitative performance. The proposed method is to achieve the required performance with fewer resources by supplementing the existing method which cannot calculate the complex shape of the perforated panel.

Keywords: building envelope, machine learning, perforated metal, multi-factor optimization, façade

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Authors: Tanu Khanuja, Harikrishnan N. Unni

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Head is the most vulnerable part of human body and may cause severe life threatening injuries. As the in vivo brain response cannot be recorded during injury, computational investigation of the head model could be really helpful to understand the injury mechanism. Majority of the physical damage to living tissues are caused by relative motion within the tissue due to tensile and shearing structural failures. The present Finite Element study focuses on investigating intracranial pressure and stress/strain distributions resulting from impact loads on various sites of human head. This is performed by the development of the 3D model of a human head with major segments like cerebrum, cerebellum, brain stem, CSF (cerebrospinal fluid), and skull from patient specific MRI (magnetic resonance imaging). The semi-automatic segmentation of head is performed using AMIRA software to extract finer grooves of the brain. To maintain the accuracy high number of mesh elements are required followed by high computational time. Therefore, the mesh optimization has also been performed using tetrahedral elements. In addition, model validation with experimental literature is performed as well. Hard tissues like skull is modeled as elastic whereas soft tissues like brain is modeled with viscoelastic prony series material model. This paper intends to obtain insights into the severity of brain injury by analyzing impacts on frontal, top, back, and temporal sites of the head. Yield stress (based on von Mises stress criterion for tissues) and intracranial pressure distribution due to impact on different sites (frontal, parietal, etc.) are compared and the extent of damage to cerebral tissues is discussed in detail. This paper finds that how the back impact is more injurious to overall head than the other. The present work would be helpful to understand the injury mechanism of traumatic brain injury more effectively.

Keywords: dynamic impact analysis, finite element analysis, intracranial pressure, MRI, traumatic brain injury, von Misses stress

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Authors: Medha Basu, Kumardeb Banerjee, Dipak Ghosh

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After the massive blow of the COVID-19 pandemic, several health hazards have been reported all over the world. Serious cases of Major Depressive Disorder (MDD) are seen to be common in about 15% of the global population, making depression one of the leading mental health diseases, as reported by the World Health Organization. Various psychological and pharmacological treatment techniques are regularly being reported. Music, a globally accepted mode of entertainment, is often used as a therapeutic measure to treat various health conditions. We have tried to understand how Indian Classical Music can affect the overall well-being of the human brain. A case study has been reported here, where a Flute-rendition has been chosen from a detailed audience response survey, and the effects of that clip on human brain conditions have been studied from both psychological and neural perspectives. Taking help from internationally-accepted depression-rating scales, two questionnaires have been designed to understand both the prolonged and immediate effect of music on various emotional states of human lives. Thereafter, from EEG experiments on 5 participants using the same clip, the parameter ‘ALAY’, alpha frontal asymmetry (alpha power difference of right and left frontal hemispheres), has been calculated. Works of Richard Davidson show that an increase in the ‘ALAY’ value indicates a decrease in depressive symptoms. Using the non-linear technique of MFDFA on EEG analysis, we have also calculated frontal asymmetry using the complexity values of alpha-waves in both hemispheres. The results show a positive correlation between both the psychological survey and the EEG findings, revealing the prominent role of music on the human brain, leading to a decrease in mental unrest and an increase in overall well-being. In this study, we plan to propose the scientific foundation of music therapy, especially from a neurocognition perspective, with appropriate neural bio-markers to understand the positive and remedial effects of music on the human brain.

Keywords: music therapy, EEG, psychological survey, frontal alpha asymmetry, wellbeing

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Authors: Kakha Nadiardze, Nana Phirosmanashvili

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The goal of this paper is to present the problems of lack of information among farmers in food safety. Global food supply chains are becoming more and more diverse, making traceability systems much harder to implement across different food markets. In this abstract, we will present our work for analyzing the key developments in Georgian food market from regulatory controls to administrative procedures to traceability technologies. Food safety and quality assurance are most problematic issues in Georgia as food trade networks become more and more complex, food businesses are under more and more pressure to ensure that their products are safe and authentic. The theme follow-up principles from farm to table must be top-of-mind for all food manufacturers, farmers and retailers. Following the E. coli breakout last year, as well as more recent cases of food mislabeling, developments in food traceability systems is essential to food businesses if they are to present a credible brand image. Alongside this are the ever-developing technologies in food traceability networks, technologies that manufacturers and retailers need to be aware of if they are to keep up with food safety regulations and avoid recall. How to examine best practice in food management is the main question in order to protect company brand through safe and authenticated food. We are working with our farmers to work with our food safety experts and technology developers throughout the food supply chain. We provide time by time food analyses on heavy metals, pesticide residues and different pollutants. We are disseminating information among farmers how the latest food safety regulations will impact the methods to use to identify risks within their products.

Keywords: food safety, GMO, LMO, E. coli, quality

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Authors: V. Pizarro-Carmona, S. Castano-Solis, M. Cortés-Carmona, J. Fraile-Ardanuy, D. Jimenez-Bermejo

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The purpose of this article is to optimize the Equivalent Electric Circuit Model (EECM) of different orders to obtain greater precision in the modeling of Li-ion battery packs. Optimization includes considering circuits based on 1RC, 2RC and 3RC networks, with a dependent voltage source and a series resistor. The parameters are obtained experimentally using tests in the time domain and in the frequency domain. Due to the high non-linearity of the behavior of the battery pack, Genetic Algorithm (GA) was used to solve and optimize the parameters of each EECM considered (1RC, 2RC and 3RC). The objective of the estimation is to minimize the mean square error between the measured impedance in the real battery pack and those generated by the simulation of different proposed circuit models. The results have been verified by comparing the Nyquist graphs of the estimation of the complex impedance of the pack. As a result of the optimization, the 2RC and 3RC circuit alternatives are considered as viable to represent the battery behavior. These battery pack models are experimentally validated using a hardware-in-the-loop (HIL) simulation platform that reproduces the well-known New York City cycle (NYCC) and Federal Test Procedure (FTP) driving cycles for electric vehicles. The results show that using GA optimization allows obtaining EECs with 2RC or 3RC networks, with high precision to represent the dynamic behavior of a battery pack in vehicular applications.

Keywords: Li-ion battery packs modeling optimized, EECM, GA, electric vehicle applications

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Authors: Rishi Prajapati

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3G is the abbreviation of third generation of wireless mobile telecommunication technologies. 3G is a mode that finds application in wireless voice telephony, mobile internet access, fixed wireless internet access, video calls and mobile TV. It also provides mobile broadband access to smartphones and mobile modems in laptops and computers. The first 3G networks were introduced in 1998, followed by 4G networks in 2008. 4G is the abbreviation of fourth generation of wireless mobile telecommunication technologies. 4G is termed to be the advanced form of 3G. 4G was firstly introduced in South Korea in 2007. Many abstracts have floated researches that depicted the diversity and similarity between the third and the fourth generation of wireless mobile telecommunications technology, whereas this abstract reflects the study that focuses on analyzing the preference between 3G versus 4G given by the elite group of the Indian society who are known as adolescents or the Millennial Generation aging from 18 years to 25 years. The Millennial Generation was chosen for this study as they have the easiest access to the latest technology. A sample size of 200 adolescents was selected and a structured survey was carried out which had several closed ended as well as open ended questions, to aggregate the result of this study. It was made sure that the effect of environmental factors on the subjects was as minimal as possible. The data analysis comprised of primary data collection reflecting it as quantitative research. The rationale behind this research is to give brief idea of how 3G and 4G are accepted by the Millennial Generation in India. The findings of this research would materialize a framework which depicts whether Millennial Generation would prefer 4G over 3G or vice versa.

Keywords: fourth generation, wireless telecommunication technology, Indian society, millennial generation, market research, third generation

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Authors: Sakshi Dhumale, Madhushree C., Amba Shetty

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The effect of station density on the geostatistical prediction of groundwater levels is of critical importance to ensure accurate and reliable predictions. Monitoring station density directly impacts the accuracy and reliability of geostatistical predictions by influencing the model's ability to capture localized variations and small-scale features in groundwater levels. This is particularly crucial in regions with complex hydrogeological conditions and significant spatial heterogeneity. Insufficient station density can result in larger prediction uncertainties, as the model may struggle to adequately represent the spatial variability and correlation patterns of the data. On the other hand, an optimal distribution of monitoring stations enables effective coverage of the study area and captures the spatial variability of groundwater levels more comprehensively. In this study, we investigate the effect of station density on the predictive performance of groundwater levels using the geostatistical technique of Ordinary Kriging. The research utilizes groundwater level data collected from 121 observation wells within the semi-arid Berambadi watershed, gathered over a six-year period (2010-2015) from the Indian Institute of Science (IISc), Bengaluru. The dataset is partitioned into seven subsets representing varying sampling densities, ranging from 15% (12 wells) to 100% (121 wells) of the total well network. The results obtained from different monitoring networks are compared against the existing groundwater monitoring network established by the Central Ground Water Board (CGWB). The findings of this study demonstrate that higher station densities significantly enhance the accuracy of geostatistical predictions for groundwater levels. The increased number of monitoring stations enables improved interpolation accuracy and captures finer-scale variations in groundwater levels. These results shed light on the relationship between station density and the geostatistical prediction of groundwater levels, emphasizing the importance of appropriate station densities to ensure accurate and reliable predictions. The insights gained from this study have practical implications for designing and optimizing monitoring networks, facilitating effective groundwater level assessments, and enabling sustainable management of groundwater resources.

Keywords: station density, geostatistical prediction, groundwater levels, monitoring networks, interpolation accuracy, spatial variability

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Authors: G. González, J. Ramírez, A. Rubiano

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This article describes different patented systems for safe use in vehicles based on GPS technology, speed sensors, gyroscopes, maps, communication systems, and monitors, that inform the driver about traffic jam, obstruction in the road, speed limits, among others. Once the information is analyzed and contrasted to final propose new technical needs to be solved.

Keywords: GPS, information technology, telecommunications, communication networks, gyroscope, environmental pollution

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Authors: Mehdi Destani, Asghar Heydari

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Objective: The present study aimed to investigate the association between pathological personality dimensions and emotion dysregulation through the mediating role of Cyberchondria behaviors among users of virtual networks. Materials and methods: A descriptive–correlational research method was used in this study, and the statistical population consisted of all people active on social network sites in 2020. The sample size was 300 people who were selected through Convenience Sampling. Data collection was carried out in a survey method using online questionnaires, including the "Difficulties in Emotion Regulation Scale" (DERS), Personality Inventory for DSM-5 Brief Form (PID-5-BF), and Cyberchondria Severity Scale Brief Form (CSS-12). Data analysis was conducted using Pearson's Correlation Coefficient and Structural Equation Modeling (SEM). Findings: Findings suggested that pathological personality dimensions and Cyberchondria behaviors have a positive and significant association with emotion dysregulation (p<0.001). The presented model had a good fit with the data. The variable “pathological personality dimensions” with an overall effect (p<0.001, β=0.658), a direct effect (p<0.001, β=0.528), and an indirect mediating effect through Cyberchondria Behaviors (p<.001), β=0.130), accounted for emotion dysregulation among virtual network users. Conclusion: The research findings showed a necessity to pay attention to the pathological personality dimensions as a determining variable and Cyberchondria behaviors as a mediator in the vulnerability of users of social network sites to emotion dysregulation.

Keywords: cyberchondria, emotion dysregulation, pathological personality dimensions, social networks

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Authors: Kawser Ahmed, K. Bousson, Milca F. Coelho

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4D flight trajectory optimization is one of the key ingredients to improve flight efficiency and to enhance the air traffic capacity in the current air traffic management (ATM). The present paper explores the iterative dynamic programming (IDP) as a potential numerical optimization method for 4D flight trajectory optimization. IDP is an iterative version of the Dynamic programming (DP) method. Due to the numerical framework, DP is very suitable to deal with nonlinear discrete dynamic systems. The 4D waypoint representation of the flight trajectory is similar to the discretization by a grid system; thus DP is a natural method to deal with the 4D flight trajectory optimization. However, the computational time and space complexity demanded by the DP is enormous due to the immense number of grid points required to find the optimum, which prevents the use of the DP in many practical high dimension problems. On the other hand, the IDP has shown potentials to deal successfully with high dimension optimal control problems even with a few numbers of grid points at each stage, which reduces the computational effort over the traditional DP approach. Although the IDP has been applied successfully in chemical engineering problems, IDP is yet to be validated in 4D flight trajectory optimization problems. In this paper, the IDP has been successfully used to generate minimum length 4D optimal trajectory avoiding any obstacle in its path, such as a no-fly zone or residential areas when flying in low altitude to reduce noise pollution.

Keywords: 4D waypoint navigation, iterative dynamic programming, obstacle avoidance, trajectory optimization

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Authors: Fatemeh Nokhodchi Bonab

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Bioinformatics, in its broad sense, involves application of computer processes to solve biological problems. A wide range of computational tools are needed to effectively and efficiently process large amounts of data being generated as a result of recent technological innovations in biology and medicine. A number of computational tools have been developed or adapted to deal with the experimental riches of complex and multivariate data and transition from data collection to information or knowledge. These bioinformatics tools are being evaluated and applied in various medical areas including early detection, risk assessment, classification, and prognosis of cancer. The goal of these efforts is to develop and identify bioinformatics methods with optimal sensitivity, specificity, and predictive capabilities. The recent flood of data from genome sequences and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science. Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying "informatics" techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale. Here we propose a definition for this new field and review some of the research that is being pursued, particularly in relation to transcriptional regulatory systems.

Keywords: methods, applications, transcriptional regulatory systems, techniques

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Authors: Wolfram Jahn, Jose Manuel Munita

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While computational fluid dynamics (CFD) simulations of fire scenarios are commonly used in the design of buildings, less attention has been given to the use of CFD simulations as an operational tool for the fire services. The reason of this lack of attention lies mainly in the fact that CFD simulations typically take large periods of time to complete, and their results would thus not be available in time to be of use during an emergency. Firefighters often face uncertain conditions when entering a building to attack a fire. They would greatly benefit from a technology based on predictive fire simulations, able to assist their decision-making process. The principal constraint to faster CFD simulations is the fine grid necessary to solve accurately the physical processes that govern a fire. This paper explores the possibility of overcoming this constraint and using coarse grid CFD simulations for fire scenarios, and proposes a methodology to use the simulation results in a meaningful way that can be used by the fire fighters during an emergency. Data from real scale compartment fire tests were used to compare CFD fire models with different grid arrangements, and empirical correlations were obtained to interpolate data points into the grids. The results show that the strongly predominant effect of the heat release rate of the fire on the fluid dynamics allows for the use of coarse grids with relatively low overall impact of simulation results. Simulations with an acceptable level of accuracy could be run in real time, thus making them useful as a forecasting tool for emergency response purposes.

Keywords: CFD, fire simulations, emergency response, forecast

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Authors: Qiming Wang

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Dynamic polymer networks (DPNs) crosslinked by dynamic bonds have received intensive attention because of their special crack-healing capability. Diverse DPNs have been synthesized using a number of dynamic bonds, including dynamic covalent bond, hydrogen bond, ionic bond, metal-ligand coordination, hydrophobic interaction, and others. Despite the promising success in the polymer synthesis, the fundamental understanding of their self-healing mechanics is still at the very beginning. Especially, a general analytical model to understand the interfacial self-healing behaviors of DPNs has not been established. Here, we develop polymer-network based analytical theories that can mechanistically model the constitutive behaviors and interfacial self-healing behaviors of DPNs. We consider that the DPN is composed of interpenetrating networks crosslinked by dynamic bonds. bonds obey a force-dependent chemical kinetics. During the self-healing process, we consider the The network chains follow inhomogeneous chain-length distributions and the dynamic polymer chains diffuse across the interface to reform the dynamic bonds, being modeled by a diffusion-reaction theory. The theories can predict the stress-stretch behaviors of original and self-healed DPNs, as well as the healing strength in a function of healing time. We show that the theoretically predicted healing behaviors can consistently match the documented experimental results of DPNs with various dynamic bonds, including dynamic covalent bonds (diarylbibenzofuranone and olefin metathesis), hydrogen bonds, and ionic bonds. We expect our model to be a powerful tool for the self-healing community to invent, design, understand, and optimize self-healing DPNs with various dynamic bonds.

Keywords: self-healing polymers, dynamic covalent bonds, hydrogen bonds, ionic bonds

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Authors: Isra Elfatih Salih Edrees, Mehmet Serdar Ufuk Türeli

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In this paper, an energy-aware method is presented, integrating energy-efficient relay-augmented techniques for correlated data routing with the goal of optimizing bottleneck throughput in wireless sensor networks. The system tackles the dual challenge of throughput optimization while considering sensor network energy consumption. A unique routing metric has been developed to enable throughput maximization while minimizing energy consumption by utilizing data correlation patterns. The paper introduces a game theoretic framework to address the NP-complete optimization problem inherent in throughput-maximizing correlation-aware routing with energy limitations. By creating an algorithm that blends energy-aware route selection strategies with the best reaction dynamics, this framework provides a local solution. The suggested technique considerably raises the bottleneck throughput for each source in the network while reducing energy consumption by choosing the best routes that strike a compromise between throughput enhancement and energy efficiency. Extensive numerical analyses verify the efficiency of the method. The outcomes demonstrate the significant decrease in energy consumption attained by the energy-efficient relay-augmented bottleneck throughput maximization technique, in addition to confirming the anticipated throughput benefits.

Keywords: correlated data aggregation, energy efficiency, game theory, relay-augmented routing, throughput maximization, wireless sensor networks

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Authors: Salim Al Jadidi, Shian Gao, Shivananda Moolya

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Computational Fluid Dynamic (CFD) technique coupled with Sub-Grid-Scale (SGS) model is used to study the flow behavior of heavy oil-water-air flow in a horizontal pipe by adapting ANSYS Fluent CFD software. The technique suitable for the transport of water-lubricated heavy viscous oil in a horizontal pipe is the Core Annular flow (CAF) technique. The present study focuses on the numerical study of CAF adapting Large Eddy Simulations (LES). The basic objective of the present study is to gain a basic knowledge of the flow behavior of heavy oil using turbulent CAF through a conventional horizontal pipe. This work also focuses on the success and applicability of LES. The simulation of heavy oil-water-air three-phase flow and two-phase flow of heavy oil–water in a conventional horizontal pipe is performed using ANSYS Fluent 16.2 software. The influence of three-phase heavy oil-water air flow in a selected pipe is affected by gravity. It is also observed from the result that the air phase and the variation in the temperature impact the behavior of the annular stream and pressure drop. Some results obtained during the study are validated with the results gained from part of the literature experiments and simulations, and the results show reasonably good agreement between the studies.

Keywords: computational fluid dynamics, gravity, heavy viscous oil, three-phase flow

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Authors: Mitchell J. Baum, Badin Gibbes, Greg Collecutt

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This study details a three-dimensional field-scale numerical investigation conducted for the Gold Coast Desalination Plant (GCDP) offshore multiport brine diffuser. Quantitative assessment of diffuser performance with regard to trajectory, dilution and mapping of seafloor concentration distributions was conducted for 100% plant operation. The quasi-steady Computational Fluid Dynamics (CFD) simulations were performed using the Reynolds averaged Navier-Stokes equations with a k-ω shear stress transport turbulence closure scheme. The study compliments a field investigation, which measured brine plume characteristics under similar conditions. CFD models used an iterative mesh in a domain with dimensions 400 m long, 200 m wide and an average depth of 24.2 m. Acoustic Doppler current profiler measurements conducted in the companion field study exhibited considerable variability over the water column. The effect of this vertical variability on simulated discharge outcomes was examined. Seafloor slope was also accommodated into the model. Ambient currents varied predominantly in the longshore direction – perpendicular to the diffuser structure. Under these conditions, the alternating port orientation of the GCDP diffuser resulted in simultaneous subjection to co-propagating and counter-propagating ambient regimes. Results from quiescent ambient simulations suggest broad agreement with empirical scaling arguments traditionally employed in design and regulatory assessments. Simulated dynamic ambient regimes showed the influence of ambient crossflow upon jet trajectory, dilution and seafloor concentration is significant. The effect of ambient flow structure and the subsequent influence on jet dynamics is discussed, along with the implications for using these different simulation approaches to inform regulatory decisions.

Keywords: computational fluid dynamics, desalination, field-scale simulation, multiport brine diffuser, negatively buoyant jet

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Authors: Dario Milani, Guido Morgenthal

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Fluid dynamic computation of wind caused forces on bluff bodies e.g light flexible civil structures or high incidence of ground approaching airplane wings, is one of the major criteria governing their design. For such structures a significant dynamic response may result, requiring the usage of small scale devices as guide-vanes in bridge design to control these effects. The focus of this paper is on the numerical simulation of the bluff body problem involving multiscale phenomena induced by small scale devices. One of the solution methods for the CFD simulation that is relatively successful in this class of applications is the Vortex Particle Method (VPM). The method is based on a grid free Lagrangian formulation of the Navier-Stokes equations, where the velocity field is modeled by particles representing local vorticity. These vortices are being convected due to the free stream velocity as well as diffused. This representation yields the main advantages of low numerical diffusion, compact discretization as the vorticity is strongly localized, implicitly accounting for the free-space boundary conditions typical for this class of FSI problems, and a natural representation of the vortex creation process inherent in bluff body flows. When the particle resolution reaches the Kolmogorov dissipation length, the method becomes a Direct Numerical Simulation (DNS). However, it is crucial to note that any solution method aims at balancing the computational cost against the accuracy achievable. In the classical VPM method, if the fluid domain is discretized by Np particles, the computational cost is O(Np2). For the coupled FSI problem of interest, for example large structures such as long-span bridges, the aerodynamic behavior may be influenced or even dominated by small structural details such as barriers, handrails or fairings. For such geometrically complex and dimensionally large structures, resolving the complete domain with the conventional VPM particle discretization might become prohibitively expensive to compute even for moderate numbers of particles. It is possible to reduce this cost either by reducing the number of particles or by controlling its local distribution. It is also possible to increase the accuracy of the solution without increasing substantially the global computational cost by computing a correction of the particle-particle interaction in some regions of interest. In this paper different strategies are presented in order to extend the conventional VPM method to reduce the computational cost whilst resolving the required details of the flow. The methods include temporal sub stepping to increase the accuracy of the particles convection in certain regions as well as dynamically re-discretizing the particle map to locally control the global and the local amount of particles. Finally, these methods will be applied on a test case and the improvements in the efficiency as well as the accuracy of the proposed extension to the method are presented. The important benefits in terms of accuracy and computational cost of the combination of these methods will be thus presented as long as their relevant applications.

Keywords: adaptation, fluid dynamic, remeshing, substepping, vortex particle method

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Authors: Bao Liu, Maarten Vanierschot, Frank Buysschaert

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The present work examines the wake dynamics of a rim-driven thruster (RDT) with Computational Fluid Dynamics (CFD). Unsteady Reynolds-averaged Navier-Stokes (URANS) equations were solved in the commercial solver ANSYS Fluent in combination with the SST k-ω turbulence model. The application of the moving reference frame (MRF) and sliding mesh (SM) approach to handling the rotational movement of the propeller were compared in the transient simulations. Validation and verification of the numerical model was performed to ensure numerical accuracy. Two representative scenarios were considered, i.e., the bollard condition (J=0) and a very light loading condition(J=0.7), respectively. From the results, it’s confirmed that compared to the SM method, the MRF method is not suitable for resolving the unsteady flow features as it only gives the general mean flow but smooths out lots of characteristic details in the flow field. By evaluating the simulation results with the SM technique, the instantaneous wake flow field under both conditions is presented and analyzed, most notably the helical vortex structure. It’s observed from the results that the tip vortices, blade shed vortices, and hub vortices are present in the wake flow field and convect downstream in a highly non-linear way. The shear layer vortices shedding from the duct displayed a strong interaction with the distorted tip vortices in an irregularmanner.

Keywords: computational fluid dynamics, rim-driven thruster, sliding mesh, wake dynamics

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Authors: Shun-Chang Yen, You-Lun Peng, Kuo-Ching San

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A numerical study on a bluff-body structure with multiple holes was conducted using ANSYS Fluent computational fluid dynamics analysis. The effects of the hole number and jet inclination angles were considered under a fixed gas flow rate and nonreactive gas. The bluff body with multiple holes can transform the axial momentum into a radial and tangential momentum as well as increase the swirl number (S). The concentration distribution in the mixing of a central carbon dioxide (CO2) jet and an annular air jet was utilized to analyze the mixing efficiency. Three bluff bodies with differing hole numbers (H = 3, 6, and 12) and three jet inclination angles (θ = 45°, 60°, and 90°) were designed for analysis. The Reynolds normal stress increases with the inclination angle. The Reynolds shear stress, average turbulence intensity, and average swirl number decrease with the inclination angle. For an unsymmetrical hole configuration (i.e., H = 3), the streamline patterns exhibited an unsymmetrical flow field. The highest mixing efficiency (i.e., the lowest integral gas fraction of CO2) occurred at H = 3. Furthermore, the highest swirl number coincided with the strongest effect on the mass fraction of CO2. Therefore, an unsymmetrical hole arrangement induced a high swirl flow behind the porous disc.

Keywords: bluff body with multiple holes, computational fluid dynamics, swirl-jet flow, mixing efficiency

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Authors: Taylan Ozturk, Osama Maklad

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This study examines the aerodynamic and flow properties of a multi-element airfoil using computational fluid dynamics (CFD) research. This computational analysis aims to optimize slat design concerning lift-drag coefficients and to determine the ideal gap size between the main airfoil and the front flap. It examines the influence of varying angles of attack and the effects of varied Reynolds numbers. A NACA 2412 airfoil, equipped with custom-designed front and rear flaps, was modeled in SolidWorks and simulated in ANSYS Fluent utilizing the k-ω SST turbulence model. This study quantifies lift and drag coefficients, turbulent kinetic energy, and vorticity magnitude across various configurations. The results clearly indicate that the slat-optimized design geometry featuring a 4 mm gap provides the best performance regarding both lift and drag, with maximum efficiency achieved at a 4-degree angle of attack. Furthermore, the results indicate the initiation of stall conditions beyond 20 degrees and demonstrate how an increase in Reynolds numbers influences flow separation and turbulence patterns. In addition, the maximum L/D ratio which is 36.18 achieved. These findings enhance the comprehension of multi-element airfoil behavior, directly impacting aircraft design and operation, particularly in high-lift situations.

Keywords: multi-element airfoil, CFD simulation, aerodynamic characteristics, Reynolds number analysis

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Authors: Wen Shyong Hsieh, Chih Hsueh Lin

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In this paper, we combine the concepts of chameleon hash function (CHF) and identification based cryptography (IBC) to build a message authentication environment for VANET under sparse RSUs. Based on the CHF, TA keeps two common secrets that will be embedded to all identities to be as the evidence of mutual trusting. TA will issue one original identity to every RSU and vehicle. An identity contains one public ID and one private key. The public ID, includes three components: pseudonym, random key, and public key, is used to present one entity and can be verified to be a legal one. The private key is used to claim the ownership of the public ID. Based on the concept of IBC, without any negotiating process, a CHF pairing key multiplied by one private key and other’s public key will be used for mutually trusting and to be utilized as the session key of secure communicating between RSUs and vehicles. To help the vehicles to do message authenticating, the RSUs are assigned to response the vehicle’s temple identity request using two short time secretes that are broadcasted by TA. To light the loading of request information, one day is divided into M time slots. At every time slot, TA will broadcast two short time secretes to all valid RSUs for that time slot. Any RSU can response the temple identity request from legal vehicles. With the collected announcement of public IDs from the neighbor vehicles, a vehicle can set up its neighboring set, which includes the information about the neighbor vehicle’s temple public ID and temple CHF pairing key that can be derived by the private key and neighbor’s public key and will be used to do message authenticating or secure communicating without the help of RSU.

Keywords: Internet of Vehicles (IOV), Vehicular Ad-hoc Networks (VANETs), Chameleon Hash Function (CHF), message authentication

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Authors: Lucas L. L. Fortes, Sandro T. M. Gonçalves

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In this work, it is analyzed the wideband performance with the mesh discretization impoverishment of the Conformal Finite Difference Time-Domain (C-FDTD) approaches developed by Raj Mittra, Supriyo Dey and Wenhua Yu for the Finite Difference Time-Domain (FDTD) method. These approaches are a simple and efficient way to optimize the scattering simulation of curved surfaces for Dielectric and Perfect Electric Conducting (PEC) structures in the FDTD method, since curved surfaces require dense meshes to reduce the error introduced due to the surface staircasing. Defined, on this work, as D-FDTD-Diel and D-FDTD-PEC, these approaches are well-known in the literature, but the improvement upon their application is not quantified broadly regarding wide frequency bands and poorly discretized meshes. Both approaches bring improvement of the accuracy of the simulation without requiring dense meshes, also making it possible to explore poorly discretized meshes which bring a reduction in simulation time and the computational expense while retaining a desired accuracy. However, their applications present limitations regarding the mesh impoverishment and the frequency range desired. Therefore, the goal of this work is to explore the approaches regarding both the wideband and mesh impoverishment performance to bring a wider insight over these aspects in FDTD applications. The D-FDTD-Diel approach consists in modifying the electric field update in the cells intersected by the dielectric surface, taking into account the amount of dielectric material within the mesh cells edges. By taking into account the intersections, the D-FDTD-Diel provides accuracy improvement at the cost of computational preprocessing, which is a fair trade-off, since the update modification is quite simple. Likewise, the D-FDTD-PEC approach consists in modifying the magnetic field update, taking into account the PEC curved surface intersections within the mesh cells and, considering a PEC structure in vacuum, the air portion that fills the intersected cells when updating the magnetic fields values. Also likewise to D-FDTD-Diel, the D-FDTD-PEC provides a better accuracy at the cost of computational preprocessing, although with a drawback of having to meet stability criterion requirements. The algorithms are formulated and applied to a PEC and a dielectric spherical scattering surface with meshes presenting different levels of discretization, with Polytetrafluoroethylene (PTFE) as the dielectric, being a very common material in coaxial cables and connectors for radiofrequency (RF) and wideband application. The accuracy of the algorithms is quantified, showing the approaches wideband performance drop along with the mesh impoverishment. The benefits in computational efficiency, simulation time and accuracy are also shown and discussed, according to the frequency range desired, showing that poorly discretized mesh FDTD simulations can be exploited more efficiently, retaining the desired accuracy. The results obtained provided a broader insight over the limitations in the application of the C-FDTD approaches in poorly discretized and wide frequency band simulations for Dielectric and PEC curved surfaces, which are not clearly defined or detailed in the literature and are, therefore, a novelty. These approaches are also expected to be applied in the modeling of curved RF components for wideband and high-speed communication devices in future works.

Keywords: accuracy, computational efficiency, finite difference time-domain, mesh impoverishment

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Authors: Satyasen Panda, Urmila Bhanja

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In this paper, we have presented and analyzed three-dimensional (3-D) matrices of wavelength/time/space code for optical code division multiple access (OCDMA) networks with NAND subtraction detection technique. The 3-D codes are constructed by integrating a two-dimensional modified quadratic congruence (MQC) code with one-dimensional modified prime (MP) code. The respective encoders and decoders were designed using fiber Bragg gratings and optical delay lines to minimize the bit error rate (BER). The performance analysis of the 3D-OCDMA system is based on measurement of signal to noise ratio (SNR), BER and eye diagram for a different number of simultaneous users. Also, in the analysis, various types of noises and multiple access interference (MAI) effects were considered. The results obtained with NAND detection technique were compared with those obtained with OR and AND subtraction techniques. The comparison results proved that the NAND detection technique with 3-D MQC\MP code can accommodate more number of simultaneous users for longer distances of fiber with minimum BER as compared to OR and AND subtraction techniques. The received optical power is also measured at various levels of BER to analyze the effect of attenuation.

Keywords: Cross Correlation (CC), Three dimensional Optical Code Division Multiple Access (3-D OCDMA), Spectral Amplitude Coding Optical Code Division Multiple Access (SAC-OCDMA), Multiple Access Interference (MAI), Phase Induced Intensity Noise (PIIN), Three Dimensional Modified Quadratic Congruence/Modified Prime (3-D MQC/MP) code

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Authors: Ethan Nikookar, Yalda Boroushaki, Larissa Statsenko, Jorge Ochoa Paniagua

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Given the serious impact that supply chain disruptions can have on a firm's bottom-line performance, both industry and academia are interested in supply chain resilience, a capability of the supply chain that enables it to cope with disruptions. To date, much of the research has focused on the antecedents of supply chain resilience. This line of research has suggested various firm-level capabilities that are associated with greater supply chain resilience. A consensus has emerged among researchers that supply chain flexibility holds the greatest potential to create resilience. Supply chain flexibility achieves resilience by creating readiness to respond to disruptions with little cost and time by means of reconfiguring supply chain resources to mitigate the impacts of the disruption. Decisions related to supply chain disruptions are made by supply chain managers; however, the role played by supply chain managers' reference networks has been overlooked in the supply chain resilience literature. This study aims to understand the impact of supply chain managers on their firms' supply chain resilience. Drawing on social capital theory and social network theory, this paper proposes a conceptual model to explore the role of supply chain managers in developing the resilience of supply chains. Our model posits that higher level of supply chain managers' embeddedness in their reference network is associated with increased resilience of their firms' supply chain. A reference network includes individuals from whom supply chain managers seek advice on supply chain related matters. The relationships between supply chain managers' embeddedness in reference network and supply chain resilience are mediated by supply chain flexibility.

Keywords: supply chain resilience, embeddedness, reference networks, social capitals

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Authors: M. Mohsin Munir, Taimoor Ahmad, Javed Munir, Usman Rashid

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Determination of maximum elevation of a flowing fluid due to sudden rejection of load in a hydropower facility is of great interest to hydraulic engineers to ensure safety of the hydraulic structures. Several mathematical models exist that employ one-dimensional modeling for the determination of surge but none of these perfectly simulate real-time circumstances. The paper envisages investigation of surge inception and propagation for a Low Head Hydropower project using Computational Fluid Dynamics (CFD) analysis on FLOW-3D software package. The fluid dynamic model utilizes its analysis for surge by employing Reynolds’ Averaged Navier-Stokes Equations (RANSE). The CFD model is designed for a case study at Taunsa hydropower Project in Pakistan. Various scenarios have run through the model keeping in view upstream boundary conditions. The prototype results were then compared with the results of physical model testing for the same scenarios. The results of the numerical model proved quite accurate coherence with the physical model testing and offers insight into phenomenon which are not apparent in physical model and shall be adopted in future for the similar low head projects limiting delays and cost incurred in the physical model testing.

Keywords: surge, FLOW-3D, numerical model, Taunsa, RANSE

Procedia PDF Downloads 357

Authors: Mohammad Hossein Modarressi, Mozhgan Mondeali, Khabat Barkhordari, Ali Etemadi

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The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant concerns worldwide due to its catastrophic effects on public health. The SARS-CoV-2 infection is initiated with the binding of the receptor-binding domain (RBD) in its spike protein to the ACE2 receptor in the host cell membrane. Due to the error-prone entity of the viral RNA-dependent polymerase complex, the virus genome, including the coding region for the RBD, acquires new mutations, leading to the appearance of multiple variants. These variants can potentially impact transmission, virulence, antigenicity and evasive immune properties. S477N mutation located in the RBD has been observed in the SARS-CoV-2 omicron (B.1.1. 529) variant. In this study, we investigated the consequences of S477N mutation at the molecular level using computational approaches such as molecular dynamics simulation, protein-protein interaction analysis, immunoinformatics and free energy computation. We showed that displacement of Ser with Asn increases the stability of the spike protein and its affinity to ACE2 and thus increases the transmission potential of the virus. This mutation changes the folding and secondary structure of the spike protein. Also, it reduces antibody neutralization, raising concern about re-infection, vaccine breakthrough and therapeutic values.

Keywords: S477N, COVID-19, molecular dynamic, SARS-COV2 mutations

Procedia PDF Downloads 172