Search results for: computational modelling

Authors: Olumide Simeon Ogunnusi, Shukor Abd Razak, Michael Kolade Adu

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Inter-agent communication manager facilitates communication among mobile agents via message passing mechanism. Until now, all Foundation for Intelligent Physical Agents (FIPA) compliant agent systems are capable of exchanging messages following the standard format of sending and receiving messages. Previous works tend to secure messages to be exchanged among a community of collaborative agents commissioned to perform specific tasks using cryptosystems. However, the approach is characterized by computational complexity due to the encryption and decryption processes required at the two ends. The proposed approach to secure agent communication allows only agents that are created by the host agent server to communicate via the agent communication channel provided by the host agent platform. These agents are assumed to be harmless. Therefore, to secure communication of legitimate agents from intrusion by external agents, a 2-phase policy enforcement system was developed. The first phase constrains the external agent to run only on the network server while the second phase confines the activities of the external agent to its execution environment. To implement the proposed policy, a controller agent was charged with the task of screening any external agent entering the local area network and preventing it from migrating to the agent execution host where the legitimate agents are running. On arrival of the external agent at the host network server, an introspector agent was charged to monitor and restrain its activities. This approach secures legitimate agent communication from Man-in-the Middle and Replay attacks.

Keywords: agent communication, introspective agent, isolation of agent, policy enforcement system

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Authors: Florian Monetti, Brett Beamsley, Peter McComb, Simon Weppe

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Land reclamation from the ocean has considerably increased over past decades to support worldwide rapid urban growth. Reshaping the coastline, however, inevitably affects coastal systems. One of the main challenges for coastal oceanographers is to predict the physical and morphological responses for nearshore systems to man-made changes over multiple time-scales. Fully-coupled numerical models are powerful tools for simulating the wide range of interactions between flow field and bedform morphology. Restricted and inconsistent measurements, combined with limited computational resources, typically make this exercise complex and uncertain. In the present study, we investigate the impact of proposed land reclamation within a wave-dominated bay in New Zealand. For this purpose, we first calibrated our morphological model based on the long-term evolution of the bay resulting from land reclamation carried out in the 1950s. This included the application of sedimentological spin-up and reduction techniques based on historical bathymetry datasets. The updated bathymetry, including the proposed modifications of the bay, was then used to predict the effect of the proposed land reclamation on the wave climate and morphology of the bay after one decade. We show that reshaping the bay induces a distinct symmetrical response of the shoreline which likely will modify the nearshore wave patterns and consequently recreational activities in the area.

Keywords: coastal waves, impact of land reclamation, long-term coastal evolution, morphodynamic modeling

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Authors: Zhuoneng Li, Zeeshan A. Rana, Karl W. Jenkins

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In industry, to validate a case, often a multitude of simulation are required and in order to demonstrate confidence in the process where users tend to use a coarser mesh. Therefore, it is imperative to establish the coarsest mesh that could be used while keeping reasonable simulation accuracy. To date, the two most reliable, affordable and broadly used advanced simulations are the hybrid RANS (Reynolds Averaged Navier Stokes)/LES (Large Eddy Simulation) and wall modelled LES. The potentials in these two simulations will still be developed in the next decades mainly because the unaffordable computational cost of a DNS (Direct Numerical Simulation). In the wall modelled LES, the turbulence model is applied as a sub-grid scale model in the most inner layer near the wall. The RANS turbulence models cover the entire boundary layer region in a hybrid RANS/LES (Detached Eddy Simulation) and its variants, therefore, the RANS still has a very important role in the state of art simulations. This research focuses on the turbulence model mesh sensitivity analysis where various turbulence models such as the S-A (Spalart-Allmaras), SSG (Speziale-Sarkar-Gatski), K-Omega transitional SST (Shear Stress Transport), K-kl-Omega, γ-Reθ transitional model, v2f are evaluated within the OpenFOAM. The simulations are conducted on a fully developed turbulent flow over a flat plate where the skin friction coefficient as well as velocity profiles are obtained to compare against experimental values and DNS results. A concrete conclusion is made to clarify the mesh sensitivity for different turbulence models.

Keywords: mesh sensitivity, turbulence models, OpenFOAM, RANS

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Authors: ShuJiang Jiang

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The interaction of turbulence with airfoil is an important noise source in many engineering fields, including helicopters, turbofan, and contra-rotating open rotor engines, where turbulence generated in the wake of upstream blades interacts with the leading edge of downstream blades and produces aerodynamic noise. One approach to study turbulence-airfoil interaction noise is to model the oncoming turbulence as harmonic gusts. A compact noise source produces a dipole-like sound directivity pattern. However, when the acoustic wavelength is much smaller than the airfoil chord length, the airfoil needs to be treated as a non-compact source, and the gust-airfoil interaction becomes more complicated and results in multiple lobes generated in the radiated sound directivity. Capturing the short acoustic wavelength is a challenge for numerical simulations. In this work, simulations are performed for gust-airfoil interaction at different Mach numbers, using a high-fidelity direct Computational AeroAcoustic (CAA) approach based on a spectral/hp element method, verified by a CAA benchmark case. It is found that the squared sound pressure varies approximately as the 5th power of Mach number, which changes slightly with the observer location. This scaling law can give a better sound prediction than the flat-plate theory for thicker airfoils. Besides, another prediction method, based on the flat-plate theory and CAA simulation, has been proposed to give better predictions than the scaling law for thicker airfoils.

Keywords: aeroacoustics, gust-airfoil interaction, CFD, CAA

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Authors: Farhan E Shafrin, Khandaker Shabbir Ahmed

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Natural ventilation strategies continue to be a key alternative to costly mechanical ventilation systems, especially in healthcare facilities, due to increasing energy issues in developing countries, including Bangladesh. Besides, overcrowding and insufficient ventilation strategies remain significant causes of thermal discomfort and hospital infection in Bangladesh. With the proper location of inlet and outlet windows, uniform flow is possible in the occupancy area to achieve thermal comfort. It also determines the airflow pattern of the ward that decreases the movement of the contaminated air. This paper aims to establish a relationship between the location of the windows and the thermal comfort of the occupants in a naturally ventilated hospital ward. It defines the openings and ventilation variables that are interrelated in a way that enhances or limits the health and thermal comfort of occupants. The study conducts a full-scale experiment in one of the naturally ventilated wards in a primary health care hospital in Manikganj, Dhaka. CFD simulation is used to explore the performance of various opening positions in ventilation efficiency and thermal comfort in the study area. The results indicate that the opening located in the hospital ward has a significant impact on the thermal comfort of the occupants and the airflow pattern inside the ward. The findings can contribute to design the naturally ventilated hospital wards by identifying and predicting future solutions when it comes to relationships with the occupants' thermal comforts.

Keywords: CFD simulation, hospital ward, natural ventilation, thermal comfort, window location

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Authors: Pitigalage Chamath Chandira Peiris

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A widely researched domain in the field of image processing in recent times has been single image super-resolution, which tries to restore a high-resolution image from a single low-resolution image. Many more single image super-resolution efforts have been completed utilizing equally traditional and deep learning methodologies, as well as a variety of other methodologies. Deep learning-based super-resolution methods, in particular, have received significant interest. As of now, the most advanced image restoration approaches are based on convolutional neural networks; nevertheless, only a few efforts have been performed using Transformers, which have demonstrated excellent performance on high-level vision tasks. The effectiveness of CNN-based algorithms in image super-resolution has been impressive. However, these methods cannot completely capture the non-local features of the data. Enhancer is a simple yet powerful Transformer-based approach for enhancing the resolution of images. A method for single image super-resolution was developed in this study, which utilized an efficient and effective transformer design. This proposed architecture makes use of a locally enhanced window transformer block to alleviate the enormous computational load associated with non-overlapping window-based self-attention. Additionally, it incorporates depth-wise convolution in the feed-forward network to enhance its ability to capture local context. This study is assessed by comparing the results obtained for popular datasets to those obtained by other techniques in the domain.

Keywords: single image super resolution, computer vision, vision transformers, image restoration

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Authors: Tal Remez, Or Litany, Alex Bronstein

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The pursuit of smaller pixel sizes at ever increasing resolution in digital image sensors is mainly driven by the stringent price and form-factor requirements of sensors and optics in the cellular phone market. Recently, Eric Fossum proposed a novel concept of an image sensor with dense sub-diffraction limit one-bit pixels (jots), which can be considered a digital emulation of silver halide photographic film. This idea has been recently embodied as the EPFL Gigavision camera. A major bottleneck in the design of such sensors is the image reconstruction process, producing a continuous high dynamic range image from oversampled binary measurements. The extreme quantization of the Poisson statistics is incompatible with the assumptions of most standard image processing and enhancement frameworks. The recently proposed maximum-likelihood (ML) approach addresses this difficulty, but suffers from image artifacts and has impractically high computational complexity. In this work, we study a variant of a sensor with binary threshold pixels and propose a reconstruction algorithm combining an ML data fitting term with a sparse synthesis prior. We also show an efficient hardware-friendly real-time approximation of this inverse operator. Promising results are shown on synthetic data as well as on HDR data emulated using multiple exposures of a regular CMOS sensor.

Keywords: binary pixels, maximum likelihood, neural networks, sparse coding

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Authors: K. Sanjayani, C. Saraswathy, S. Sreenivasan, S. Sudhahar, D. Suganya, K. S. Neelukumari, N. Vijayarangan

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Double Base Number System (DBNS) is an imminent system of representing a number using two bases namely 2 and 3, which has its application in Elliptic Curve Cryptography (ECC) and Digital Signature Algorithm (DSA).The previous binary method representation included only base 2. DBNS uses an approximation algorithm namely, Greedy Algorithm. By using this algorithm, the number of digits required to represent a larger number is less when compared to the standard binary method that uses base 2 algorithms. Hence, the computational speed is increased and time being reduced. The standard binary method uses binary digits 0 and 1 to represent a number whereas the DBNS method uses binary digit 1 alone to represent any number (canonical form). The greedy algorithm uses two ways to represent the number, one is by using only the positive summands and the other is by using both positive and negative summands. In this paper, arithmetic operations are used for elliptic curve cryptography. Elliptic curve discrete logarithm problem is the foundation for most of the day to day elliptic curve cryptography. This appears to be a momentous hard slog compared to digital logarithm problem. In elliptic curve digital signature algorithm, the key generation requires 160 bit of data by usage of standard binary representation. Whereas, the number of bits required generating the key can be reduced with the help of double base number representation. In this paper, a new technique is proposed to generate key during encryption and extraction of key in decryption.

Keywords: cryptography, double base number system, elliptic curve cryptography, elliptic curve digital signature algorithm

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Authors: Melvin Sethobya, Sithule Xanga, Sechaba Lenong, Lunga Nolakana, Gbenga Adesola

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Mining has been the cornerstone of the South African economy for the last century. Most of the gold mining in South Africa was conducted within the Witwatersrand basin, which contributed to the rapid growth of the city of Johannesburg and capitulated the city to becoming the business and wealth capital of the country. But with gradual depletion of resources, a stoppage in the extraction of underground water from mines and other factors relating to survival of the mining operations over a lengthy period, most of the mines were abandoned and left to pollute the local waterways and groundwater with toxins, heavy metal residue and increased acid mine drainage ensued. The Department of Mineral Resources and Energy commissioned a project whose aim is to monitor, maintain, and mitigate the adverse environmental impacts of polluted water mine water flowing into local streams affecting local ecosystems and livelihoods downstream. As part of mitigation efforts, the diagnosis and monitoring of groundwater or surface water polluted sites has become important. Geophysical surveys, in particular, Resistivity and Magnetics surveys, were selected as some of most suitable techniques for investigation of local ingress points along of one the major streams cutting through the Witwatersrand basin, namely the Blesbokspruit, which is found in the eastern part of the basin. The aim of the surveys was to provide information that could be used to assist in determining possible water loss/ ingress from the Blesbokspriut stream. Modelling of geophysical surveys results offered an in-depth insight into the interaction and pathways of polluted water through mapping of possible ingress channels near the Blesbokspruit. The resistivity - depth profile of the surveyed site exhibit a three(3) layered model with low resistivity values (10 to 200 Ω.m) overburden, which is underlain by a moderate resistivity weathered layer (>300 Ω.m), which sits on a more resistive crystalline bedrock (>500 Ω.m). Two locations of potential ingress channels were mapped across the two traverses at the site. The magnetic survey conducted at the site mapped a major NE-SW trending regional linearment with a strong magnetic signature, which was modeled to depth beyond 100m, with the potential to act as a conduit for dispersion of stream water away from the stream, as it shared a similar orientation with the potential ingress channels as mapped using the resistivity method.

Keywords: eletrictrical resistivity, magnetics survey, blesbokspruit, ingress

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Authors: Madhu Aneja, Sapna Sharma

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Thermal conductivity of ordinary heat transfer fluids is not adequate to meet today’s cooling rate requirements. Nanoparticles have been shown to increase the thermal conductivity and convective heat transfer to the base fluids. One of the possible mechanisms for anomalous increase in the thermal conductivity of nanofluids is the Brownian motions of the nanoparticles in the basefluid. In this paper, the natural convection of incompressible nanofluid over a nonlinear stretching sheet in the presence of magnetic field is studied. The flow and heat transfer induced by stretching sheets is important in the study of extrusion processes and is a subject of considerable interest in the contemporary literature. Appropriate similarity variables are used to transform the governing nonlinear partial differential equations to a system of nonlinear ordinary (similarity) differential equations. For computational purpose, Finite Element Method is used. The effective thermal conductivity and viscosity of nanofluid are calculated by KKL (Koo – Klienstreuer – Li) correlation. In this model effect of Brownian motion on thermal conductivity is considered. The effect of important parameter i.e. nonlinear parameter, volume fraction, Hartmann number, heat source parameter is studied on velocity and temperature. Skin friction and heat transfer coefficients are also calculated for concerned parameters.

Keywords: Brownian motion, convection, finite element method, magnetic field, nanofluid, stretching sheet

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Authors: Mohd Khairul Bazli Mohd Aziz, Fadhilah Yusof, Zulkifli Yusop, Zalina Mohd Daud, Mohammad Afif Kasno

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Recent developed rainfall network design techniques have been discussed and compared by many researchers worldwide due to the demand of acquiring higher levels of accuracy from collected data. In many studies, rain-gauge networks are designed to provide good estimation for areal rainfall and for flood modelling and prediction. In a certain study, even using lumped models for flood forecasting, a proper gauge network can significantly improve the results. Therefore existing rainfall network in Johor must be optimized and redesigned in order to meet the required level of accuracy preset by rainfall data users. The well-known geostatistics method (variance-reduction method) that is combined with simulated annealing was used as an algorithm of optimization in this study to obtain the optimal number and locations of the rain gauges. Rain gauge network structure is not only dependent on the station density; station location also plays an important role in determining whether information is acquired accurately. The existing network of 84 rain gauges in Johor is optimized and redesigned by using rainfall, humidity, solar radiation, temperature and wind speed data during monsoon season (November – February) for the period of 1975 – 2008. Three different semivariogram models which are Spherical, Gaussian and Exponential were used and their performances were also compared in this study. Cross validation technique was applied to compute the errors and the result showed that exponential model is the best semivariogram. It was found that the proposed method was satisfied by a network of 64 rain gauges with the minimum estimated variance and 20 of the existing ones were removed and relocated. An existing network may consist of redundant stations that may make little or no contribution to the network performance for providing quality data. Therefore, two different cases were considered in this study. The first case considered the removed stations that were optimally relocated into new locations to investigate their influence in the calculated estimated variance and the second case explored the possibility to relocate all 84 existing stations into new locations to determine the optimal position. The relocations of the stations in both cases have shown that the new optimal locations have managed to reduce the estimated variance and it has proven that locations played an important role in determining the optimal network.

Keywords: geostatistics, simulated annealing, semivariogram, optimization

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Authors: Assel Thami Lahlou, Soufiane Stouti, Ismail Lagrat, Hamid Mounir, Oussama Bouazaoui

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The purpose of this research work is to predict the helicopter from stalling by finding the minimum and maximum values that the pitch angle can take in order to fly in a hover state condition. The stall of a helicopter in hover occurs when the pitch angle is too small to generate the thrust required to support its weight, or when the critical angle of attack that gives maximum lift is reached or exceeded. In order to find the minimum pitch angle, a 3D CFD simulation was done in this work using ANSYS FLUENT as the CFD solver. We started with a small value of the pitch angle θ, and we kept increasing its value until we found the thrust coefficient required to fly in a hover state and support the weight of the helicopter. For the CFD analysis, the Multiple Reference Frame (MRF) method with k-ε turbulent model was used to study the 3D flow around the rotor for θmin. On the other hand, a 2D simulation of the airfoil NACA 0012 was executed with a velocity inlet Vin=ΩR/2 to visualize the flow at the location span R/2 of the disk rotor using the Spallart-Allmaras turbulent model. Finding the critical angle of attack at this position will give us the ability to predict the stall in hover flight. The results obtained will be exposed later in the article. This study was so useful to analyze the limitations of the helicopter’s main rotor and thus to predict accidents that can lead to a lot of damages.

Keywords: aerodynamic, CFD, helicopter, stall, blades, main rotor, minimum pitch angle, maximum pitch angle

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Authors: Antti Lanki, Justiina Halonen, Juuso Punnonen, Emmi Rantavuo

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Marine oil spill response operation requires extensive vessel maneuvering and navigation skills. At-sea oil containment and recovery include both single vessel and multi-vessel operations. Towing long oil containment booms that are several hundreds of meters in length, is a challenge in itself. Boom deployment and towing in multi-vessel configurations is an added challenge that requires precise coordination and control of the vessels. Efficient communication, as a prerequisite for shared situational awareness, is needed in order to execute the response task effectively. To gain and maintain adequate maritime skills, practical training is needed. Field exercises are the most effective way of learning, but especially the related vessel operations are resource-intensive and costly. Field exercises may also be affected by environmental limitations such as high sea-state or other adverse weather conditions. In Finland, the seasonal ice-coverage also limits the training period to summer seasons only. In addition, environmental sensitiveness of the sea area restricts the use of real oil or other target substances. This paper examines, whether maritime simulator training can offer a complementary method to overcome the training challenges related to field exercises. The objective is to assess the efficiency and the learning impact of simulator training, and the specific skills that can be trained most effectively in simulators. This paper provides an overview of learning results from two oil spill response pilot courses, in which maritime navigational bridge simulators were used to train the oil spill response authorities. The simulators were equipped with an oil spill functionality module. The courses were targeted at coastal Fire and Rescue Services responsible for near shore oil spill response in Finland. The competence levels of the participants were surveyed before and after the course in order to measure potential shifts in competencies due to the simulator training. In addition to the quantitative analysis, the efficiency of the simulator training is evaluated qualitatively through feedback from the participants. The results indicate that simulator training is a valid and effective method for developing marine oil spill response competencies that complement traditional field exercises. Simulator training provides a safe environment for assessing various oil containment and recovery tactics. One of the main benefits of the simulator training was found to be the immediate feedback the spill modelling software provides on the oil spill behaviour as a reaction to response measures.

Keywords: maritime training, oil spill response, simulation, vessel manoeuvring

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Authors: Ogemdi Chinwendu Anika, Anna Strzelecka, Yadira Bajón-Fernández, Raffaella Villa

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Anaerobic digestion (AD) has been used since time in memorial to take care of organic wastes in the environment, especially for sewage and wastewater treatments. Recently, the rising demand/need to increase renewable energy from organic matter has caused the AD substrates spectrum to expand and include a wider variety of organic materials such as agricultural residues and farm manure which is annually generated at around 140 billion metric tons globally. The problem, however, is that agricultural wastes are composed of materials that are heterogeneous and too difficult to degrade -particularly lignin, that make up about 0–40% of the total lignocellulose content. This study aimed to evaluate the impact of varying concentrations of lignin on biogas yields and their subsequent response to a commercial yeast-based bioadditive in batch anaerobic digesters. The experiments were carried out in batches for a retention time of 56 days with different lignin concentrations (200 mg, 300 mg, 400 mg, 500 mg, and 600 mg) treated to different conditions to first determine the concentration of the bioadditive that was most optimal for overall process improvement and yields increase. The batch experiments were set up using 130 mL bottles with a working volume of 60mL, maintained at 38°C in an incubator shaker (150rpm). Digestate obtained from a local plant operating at mesophilic conditions was used as the starting inoculum, and commercial kraft lignin was used as feedstock. Biogas measurements were carried out using the displacement method and were corrected to standard temperature and pressure using standard gas equations. Furthermore, the modified Gompertz equation model was used to non-linearly regress the resulting data to estimate gas production potential, production rates, and the duration of lag phases as indicatives of degrees of lignin inhibition. The results showed that lignin had a strong inhibitory effect on the AD process, and the higher the lignin concentration, the more the inhibition. Also, the modelling showed that the rates of gas production were influenced by the concentrations of the lignin substrate added to the system – the higher the lignin concentrations in mg (0, 200, 300, 400, 500, and 600) the lower the respective rate of gas production in ml/gVS.day (3.3, 2.2, 2.3, 1.6, 1.3, and 1.1), although the 300 mg increased by 0.1 ml/gVS.day over that of the 200 mg. The impact of the yeast-based bioaddition on the rate of production was most significant in the 400 mg and 500 mg as the rate was improved by 0.1 ml/gVS.day and 0.2 ml/gVS.day respectively. This indicates that agricultural residues with higher lignin content may be more responsive to inhibition alleviation by yeast-based bioadditive; therefore, further study on its application to the AD of agricultural residues of high lignin content will be the next step in this research.

Keywords: anaerobic digestion, renewable energy, lignin valorisation, biogas

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Authors: Dangut Maren David, Skaf Zakwan

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Adequate monitoring of vehicle component in other to obtain high uptime is the goal of predictive maintenance, the major challenge faced by businesses in industries is the significant cost associated with a delay in service delivery due to system downtime. Most of those businesses are interested in predicting those problems and proactively prevent them in advance before it occurs, which is the core advantage of Prognostic Health Management (PHM) application. The recent emergence of industry 4.0 or industrial internet of things (IIoT) has led to the need for monitoring systems activities and enhancing system-to-system or component-to- component interactions, this has resulted to a large generation of data known as big data. Analysis of big data represents an increasingly important, however, due to complexity inherently in the dataset such as imbalance classification problems, it becomes extremely difficult to build a model with accurate high precision. Data-driven predictive modeling for condition-based maintenance (CBM) has recently drowned research interest with growing attention to both academics and industries. The large data generated from industrial process inherently comes with a different degree of complexity which posed a challenge for analytics. Thus, imbalance classification problem exists perversely in industrial datasets which can affect the performance of learning algorithms yielding to poor classifier accuracy in model development. Misclassification of faults can result in unplanned breakdown leading economic loss. In this paper, an advanced approach for handling imbalance classification problem is proposed and then a prognostic model for predicting aircraft component replacement is developed to predict component replacement in advanced by exploring aircraft historical data, the approached is based on hybrid ensemble-based method which improves the prediction of the minority class during learning, we also investigate the impact of our approach on multiclass imbalance problem. We validate the feasibility and effectiveness in terms of the performance of our approach using real-world aircraft operation and maintenance datasets, which spans over 7 years. Our approach shows better performance compared to other similar approaches. We also validate our approach strength for handling multiclass imbalanced dataset, our results also show good performance compared to other based classifiers.

Keywords: prognostics, data-driven, imbalance classification, deep learning

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Authors: Priscilla Echeverria

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This paper addresses initial teacher formation as a formative space in which pedagogy students develop a pedagogical agency capacity to contribute to social justice, considering ethical, political, and epistemic dimensions. This paper is structured by discussing first the concepts of agency, pedagogical interaction, and social justice from a critical perspective; and continues offering preliminary results on the capacity of pedagogical agency in novice teachers after the analysis of critical incidents as a research methodology. This study is motivated by the concern that responding to the current neoliberal scenario, many initial teacher formation (ITF) programs have reduced the meaning of education to instruction, and pedagogy to methodology, favouring the formation of a technical professional over a reflective or critical one. From this concern, this study proposes that the restitution of the subject is an urgent task in teacher formation, so it is essential to enable him in his capacity for action and advance in eliminating institutionalized oppression insofar as it affects that capacity. Given that oppression takes place in human interaction, through this work, I propose that initial teacher formation develops sensitivity and educates the gaze to identify oppression and take action against it, both in pedagogical interactions -which configure political, ethical, and epistemic subjectivities- as in the hidden and official curriculum. All this from the premise that modelling democratic and dialogical interactions are basic for any program that seeks to contribute to a more just and empowered society. The contribution of this study lies in the fact that it opens a discussion in an area about which we know little: the impact of the type of interactions offered by university teaching at ITF on the capacity of future teachers to be pedagogical agents. For this reason, this study seeks to gather evidence of the result of this formation, analysing the capacity of pedagogical agency of novice teachers, or, in other words, how capable the graduates of secondary pedagogies are in their first pedagogical experiences to act and make decisions putting the formative purposes that they are capable of autonomously defining before technical or bureaucratic issues imposed by the curriculum or the official culture. This discussion is part of my doctoral research, "The importance of developing the capacity for ethical-political-epistemic agency in novice teachers during initial teacher formation to contribute to social justice", which I am currently developing in the Educational Research program of the University of Lancaster, United Kingdom, as a Conicyt fellow for the 2019 cohort.

Keywords: initial teacher formation, pedagogical agency, pedagogical interaction, social justice, hidden curriculum

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Authors: Behzad Ahdiharab, Senol Baskaya, Tamer Calisir

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Heat exchangers are one of the most widely used systems in factories, refineries etc. In this study, natural convection heat transfer using nano-fluids in between two cylinders is numerically investigated. The inner and outer cylinders are kept at constant temperatures. One of the most important assumptions in the project is that the working fluid is non-Newtonian. In recent years, the use of nano-fluids in industrial applications has increased profoundly. In this study, nano-Newtonian fluids containing metal particles with high heat transfer coefficients have been used. All fluid properties such as homogeneity has been calculated. In the present study, solutions have been obtained under unsteady conditions, base fluid was water, and effects of various parameters on heat transfer have been investigated. These parameters are Rayleigh number (103 < Ra < 106), power-law index (0.6 < n < 1.4), aspect ratio (0 < AR < 0.8), nano-particle composition, horizontal and vertical displacement of the inner cylinder, rotation of the inner cylinder, and volume fraction of nanoparticles. Results such as the internal cylinder average and local Nusselt number variations, contours of temperature, flow lines are presented. The results are also discussed in detail. From the validation study performed it was found that a very good agreement exists between the present results and those from the open literature. It was found out that the heat transfer is always affected by the investigated parameters. However, the degree to which the heat transfer is affected does change in a wide range.

Keywords: heat transfer, circular space, non-Newtonian, nano fluid, computational fluid dynamics.

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Authors: Tsui-Tsai Lin

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Cooperative orthogonal frequency division multiplexing (OFDM) transmission, which possesses the advantages of better connectivity, expanded coverage, and resistance to frequency selective fading, has been a more powerful solution for the physical layer in wireless communications. However, such a hybrid scheme suffers from the carrier frequency offset (CFO) effects inherited from the OFDM-based systems, which lead to a significant degradation in performance. In addition, insertion of a cyclic prefix (CP) at each symbol block head for combating inter-symbol interference will lead to a reduction in spectral efficiency. The design on the CFO estimation for the cooperative OFDM system without CP is a suspended problem. This motivates us to develop a low complexity CFO estimator for the cooperative OFDM decode-and-forward (DF) communication system without CP over the multipath fading channel. Especially, using a block-type pilot, the CFO estimation is first derived in accordance with the least square criterion. A reliable performance can be obtained through an exhaustive two-dimensional (2D) search with a penalty of heavy computational complexity. As a remedy, an alternative solution realized with an iteration approach is proposed for the CFO estimation. In contrast to the 2D-search estimator, the iterative method enjoys the advantage of the substantially reduced implementation complexity without sacrificing the estimate performance. Computer simulations have been presented to demonstrate the efficacy of the proposed CFO estimation.

Keywords: cooperative transmission, orthogonal frequency division multiplexing (OFDM), carrier frequency offset, iteration

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Authors: J. P. Panda, K. Sasmal, H. V. Warrior

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Eddy viscosity models in turbulence modeling can be mainly classified as linear and nonlinear models. Linear formulations are simple and require less computational resources but have the disadvantage that they cannot predict actual flow pattern in complex geophysical flows where streamline curvature and swirling motion are predominant. A constitutive equation of Reynolds stress anisotropy is adopted for the formulation of eddy viscosity including all the possible higher order terms quadratic in the mean velocity gradients, and a simplified model is developed for actual oceanic flows where only the vertical velocity gradients are important. The new model is incorporated into the one dimensional General Ocean Turbulence Model (GOTM). Two realistic oceanic test cases (OWS Papa and FLEX' 76) have been investigated. The new model predictions match well with the observational data and are better in comparison to the predictions of the two equation k-epsilon model. The proposed model can be easily incorporated in the three dimensional Princeton Ocean Model (POM) to simulate a wide range of oceanic processes. Practically, this model can be implemented in the coastal regions where trasverse shear induces higher vorticity, and for prediction of flow in estuaries and lakes, where depth is comparatively less. The model predictions of marine turbulence and other related data (e.g. Sea surface temperature, Surface heat flux and vertical temperature profile) can be utilized in short term ocean and climate forecasting and warning systems.

Keywords: Eddy viscosity, turbulence modeling, GOTM, CFD

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Authors: Sajjad Hussain, Sabir Khan, Maria Del Pilar Taboada Sotomayor

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This work demonstrates the synthesis of magnetic molecularly imprinted polymers (MMIPs) for determination of a selected pesticide (ametryne) using high performance liquid chromatography (HPLC). Computational simulation can assist the choice of the most suitable monomer for the synthesis of polymers. The (MMIPs) were polymerized at the surface of Fe3O4@SiO2 magnetic nanoparticles (MNPs) using 2-vinylpyradine as functional monomer, ethylene-glycol-dimethacrylate (EGDMA) is a cross-linking agent and 2,2-Azobisisobutyronitrile (AIBN) used as radical initiator. Magnetic non-molecularly imprinted polymer (MNIPs) was also prepared under the same conditions without analyte. The MMIPs were characterized by scanning electron microscopy (SEM), Brunauer, Emmett and Teller (BET) and Fourier transform infrared spectroscopy (FTIR). Pseudo first order and pseudo second order model were applied to study kinetics of adsorption and it was found that adsorption process followed the pseudo first order kinetic model. Adsorption equilibrium data was fitted to Freundlich and Langmuir isotherms and the sorption equilibrium process was well described by Langmuir isotherm mode. The selectivity coefficients (α) of MMIPs for ametryne with respect to atrazine, ciprofloxacin and folic acid were 4.28, 12.32, and 14.53 respectively. The spiked recoveries ranged between 91.33 and 106.80% were obtained. The results showed high affinity and selectivity of MMIPs for pesticide ametryne in the food samples.

Keywords: molecularly imprinted polymer, pesticides, magnetic nanoparticles, adsorption

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Authors: Sebastian Kohlstädt, Michael Vynnycky, Stephan Goeke, Jan Jäckel, Andreas Gebauer-Teichmann

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This paper summarizes the progress in the latest computational fluid dynamics research towards the modeling in of lost core viability in high-pressure die casting. High-pressure die casting is a process that is widely employed in the automotive and neighboring industries due to its advantages in casting quality and cost efficiency. The degrees of freedom are however somewhat limited as it has been so far difficult to use lost cores in the process. This is right now changing and the deployment of lost cores is considered a future growth potential for high-pressure die casting companies. The use of this technology itself is difficult though. The strength of the core material, as chiefly salt is used, is limited and experiments have shown that the cores will not hold under all circumstances and process designs. For this purpose, the publicly available CFD library foam-extend (OpenFOAM) is used, and two additional fluid models for incompressible and compressible two-phase flow are implemented as fluid solver models into the FSI library. For this purpose, the volume-of-fluid (VOF) methodology is used. The necessity for the fluid-structure interaction (FSI) approach is shown by a simple CFD model geometry. The model is benchmarked against analytical models and experimental data. Sufficient agreement is found with the analytical models and good agreement with the experimental data. An outlook on future developments concludes the paper.

Keywords: CFD, fluid-structure interaction, high-pressure die casting, multiphase flow

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Authors: M. A. Askari, M. A. Nazari, P. Perrier, Y. Payan

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Growth and remodeling of biological structures have gained lots of attention over the past decades. Determining the response of living tissues to mechanical loads is necessary for a wide range of developing fields such as prosthetics design or computerassisted surgical interventions. It is a well-known fact that biological structures are never stress-free, even when externally unloaded. The exact origin of these residual stresses is not clear, but theoretically, growth is one of the main sources. Extracting body organ’s shapes from medical imaging does not produce any information regarding the existing residual stresses in that organ. The simplest cause of such stresses is gravity since an organ grows under its influence from birth. Ignoring such residual stresses might cause erroneous results in numerical simulations. Accounting for residual stresses due to tissue growth can improve the accuracy of mechanical analysis results. This paper presents an original computational framework based on gradual growth to determine the residual stresses due to growth. To illustrate the method, we apply it to a finite element model of a healthy human face reconstructed from medical images. The distribution of residual stress in facial tissues is computed, which can overcome the effect of gravity and maintain tissues firmness. Our assumption is that tissue wrinkles caused by aging could be a consequence of decreasing residual stress and thus not counteracting gravity. Taking into account these stresses seems therefore extremely important in maxillofacial surgery. It would indeed help surgeons to estimate tissues changes after surgery.

Keywords: finite element method, growth, residual stress, soft tissue

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Authors: Mohamed Lotfy, Ghada Soliman

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Recognizing Arabic dot-matrix digits is a challenging problem due to the unique characteristics of dot-matrix fonts, such as irregular dot spacing and varying dot sizes. This paper presents an approach for recognizing Arabic digits printed in dot matrix format. The proposed model is based on Convolutional Neural Networks (CNN) that take the dot matrix as input and generate embeddings that are rounded to generate binary representations of the digits. The binary embeddings are then used to perform Optical Character Recognition (OCR) on the digit images. To overcome the challenge of the limited availability of dotted Arabic expiration date images, we developed a True Type Font (TTF) for generating synthetic images of Arabic dot-matrix characters. The model was trained on a synthetic dataset of 3287 images and 658 synthetic images for testing, representing realistic expiration dates from 2019 to 2027 in the format of yyyy/mm/dd. Our model achieved an accuracy of 98.94% on the expiry date recognition with Arabic dot matrix format using fewer parameters and less computational resources than traditional CNN-based models. By investigating and presenting our findings comprehensively, we aim to contribute substantially to the field of OCR and pave the way for advancements in Arabic dot-matrix character recognition. Our proposed approach is not limited to Arabic dot matrix digit recognition but can also be extended to text recognition tasks, such as text classification and sentiment analysis.

Keywords: computer vision, pattern recognition, optical character recognition, deep learning

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Authors: Tetyana Kendzerska, David T. Zhu, Michael Pugliese, Douglas Manuel, Mohsen Sadatsafavi, Marcus Povitz, Therese A. Stukel, Teresa To, Shawn D. Aaron, Sunita Mulpuru, Melanie Chin, Claire E. Kendall, Kednapa Thavorn, Rebecca Robillard, Andrea S. Gershon

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The impact of the COVID-19 pandemic on the management of ambulatory care sensitive conditions (ACSCs) remains unknown. To compare observed and expected (projected based on previous years) trends in all-cause mortality and healthcare use for ACSCs in the first year of the pandemic (March 2020 - March 2021). A population-based study using provincial health administrative data.General adult population (Ontario, Canada). Monthly all-cause mortality, and hospitalizations, emergency department (ED) and outpatient visit rates (per 100,000 people at-risk) for seven combined ACSCs (asthma, COPD, angina, congestive heart failure, hypertension, diabetes, and epilepsy) during the first year were compared with similar periods in previous years (2016-2019) by fitting monthly time series auto-regressive integrated moving-average models. Compared to previous years, all-cause mortality rates increased at the beginning of the pandemic (observed rate in March-May 2020 of 79.98 vs. projected of 71.24 [66.35-76.50]) and then returned to expected in June 2020—except among immigrants and people with mental health conditions where they remained elevated. Hospitalization and ED visit rates for ACSCs remained lower than projected throughout the first year: observed hospitalization rate of 37.29 vs. projected of 52.07 (47.84-56.68); observed ED visit rate of 92.55 vs. projected of 134.72 (124.89-145.33). ACSC outpatient visit rates decreased initially (observed rate of 4,299.57 vs. projected of 5,060.23 [4,712.64-5,433.46]) and then returned to expected in June 2020. Reductions in outpatient visits for ACSCs at the beginning of the pandemic combined with reduced hospital admissions may have been associated with temporally increased mortality—disproportionately experienced by immigrants and those with mental health conditions. The Ottawa Hospital Academic Medical Organization

Keywords: COVID-19, chronic disease, all-cause mortality, hospitalizations, emergency department visits, outpatient visits, modelling, population-based study, asthma, COPD, angina, heart failure, hypertension, diabetes, epilepsy

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Authors: Sadeq Al Yaari, Fayza Al Hammadi, Ayman Al Yaari, Adham Al Yaari, Montaha Al Yaari, Aayah Al Yaari, Sajedah Al Yaari, Salah Al Yami

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Background: When we further take Arabic grammatical issues into account in accordance with applied linguistic investigations on Arabic as Second Language (ASL) learners, a fundamental issue arises at this point as to the production of speech in Arabic: Oral grammatical errors committed by ASL learners. Aims: Using manual rating as well as computational analytic methodology to test a corpus of recorded speech by Second Language (ASL) learners of Arabic, this study aims to find the areas of difficulties in learning Arabic grammar. More specifically, it examines how and why ASL learners make grammatical errors in their oral speech. Methods: Tape recordings of four (4) Arabic as Second Language (ASL) learners who ranged in age from 23 to 30 were naturally collected. All participants have completed an intensive Arabic program (two years) and 20 minute-speech was recorded for each participant. Having the collected corpus, the next procedure was to rate them against Arabic standard grammar. The rating includes four processes: Description, analysis and assessment. Conclusions: Outcomes made from the issues addressed in this paper can be summarized in the fact that ASL learners face many grammatical difficulties when studying Arabic word order, tenses and aspects, function words, subject-verb agreement, verb form, active-passive voice, global and local errors, processes-based errors including addition, omission, substitution or a combination of any of them.

Keywords: grammar, error, oral, Arabic, second language, learner, applied linguistics.

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Authors: Mohamed Razi Nalim, Shahrzad Ghadiri

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With current concerns regarding global warming, demand for a society with greater environmental awareness significantly increases. With gradual development in hybrid and electric vehicles and the availability of renewable energy resources, increasing efficiency in fossil fuel and combustion engines seems a faster solution toward sustainability and reducing greenhouse gas emissions. This paper aims to provide a comprehensive review of recent progress in wave rotor combustor, one of the combustion concepts with considerable potential to improve power output and emission standards. A wave rotor is an oscillatory flow device that uses the unsteady gas dynamic concept to transfer energy by generating pressure waves. From a thermodynamic point of view, unlike conventional positive-displacement piston engines which follow the Brayton cycle, wave rotors offer higher cycle efficiency due to pressure gain during the combustion process based on the Humphrey cycle. First, the paper covers all recent and ongoing computational and experimental studies around the world with a quick look at the milestones in the history of wave rotor development. Second, the main similarity and differences in the ignition system of the wave rotor with piston engines are considered. Also, the comparison is made with another pressure gain device, rotating detonation engines. Next, the main challenges and research needs for wave rotor combustor commercialization are discussed.

Keywords: wave rotor combustor, unsteady gas dynamic, pre-chamber jet ignition, pressure gain combustion, constant-volume combustion

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Authors: J. S. Dhillon, K. K. Dhaliwal

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In almost all the digital filtering applications the digital infinite impulse response (IIR) filters are preferred over finite impulse response (FIR) filters because they provide much better performance, less computational cost and have smaller memory requirements for similar magnitude specifications. However, the digital IIR filters are generally multimodal with respect to the filter coefficients and therefore, reliable methods that can provide global optimal solutions are required. The artificial bee colony (ABC) algorithm is one such recently introduced meta-heuristic optimization algorithm. But in some cases it shows insufficiency while searching the solution space resulting in a weak exchange of information and hence is not able to return better solutions. To overcome this deficiency, the opposition based learning strategy is incorporated in ABC and hence a modified version called oppositional artificial bee colony (OABC) algorithm is proposed in this paper. Duplication of members is avoided during the run which also augments the exploration ability. The developed algorithm is then applied for the design of optimal and stable digital IIR filter structure where design of low-pass (LP) and high-pass (HP) filters is carried out. Fuzzy theory is applied to achieve maximize satisfaction of minimum magnitude error and stability constraints. To check the effectiveness of OABC, the results are compared with some well established filter design techniques and it is observed that in most cases OABC returns better or atleast comparable results.

Keywords: digital infinite impulse response filter, artificial bee colony optimization, opposition based learning, digital filter design, multi-parameter optimization

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Authors: Nishant Rodrigues, Nicole Spanedda, Chilukuri K. Mohan, Arindam Chakraborty

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A crucial objective in quantum chemistry is the computation of the energy levels of chemical systems. This task requires electron-repulsion integrals as inputs, and the steep computational cost of evaluating these integrals poses a major numerical challenge in efficient implementation of quantum chemical software. This work presents a moment-based machine-learning approach for the efficient evaluation of electron-repulsion integrals. These integrals were approximated using linear combinations of a small number of moments. Machine learning algorithms were applied to estimate the coefficients in the linear combination. A random forest approach was used to identify promising features using a recursive feature elimination approach, which performed best for learning the sign of each coefficient but not the magnitude. A neural network with two hidden layers were then used to learn the coefficient magnitudes along with an iterative feature masking approach to perform input vector compression, identifying a small subset of orbitals whose coefficients are sufficient for the quantum state energy computation. Finally, a small ensemble of neural networks (with a median rule for decision fusion) was shown to improve results when compared to a single network.

Keywords: quantum energy calculations, atomic orbitals, electron-repulsion integrals, ensemble machine learning, random forests, neural networks, feature extraction

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Authors: Hilal Naimi, Amelbahahouda Adamou-Mitiche, Lahcene Mitiche

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The main problem in the area of medical imaging has been image denoising. The most defying for image denoising is to secure data carrying structures like surfaces and edges in order to achieve good visual quality. Different algorithms with different denoising performances have been proposed in previous decades. More recently, models focused on deep learning have shown a great promise to outperform all traditional approaches. However, these techniques are limited to the necessity of large sample size training and high computational costs. This research proposes a denoising approach basing on LDTCWT (Lifting Dual Tree Complex Wavelet Transform) using Hybrid Thresholding with Wiener filter to enhance the quality image. This research describes the LDTCWT as a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). To develop this approach, a hybrid thresholding function is modeled by integrating the Wiener filter into the thresholding function.

Keywords: lifting wavelet transform, image denoising, dual tree complex wavelet transform, wavelet shrinkage, wiener filter

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Authors: Syed Muhammad Awais Tahir, Syed Hossein Raza Hamdani

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In the last two years, there has been a substantial increase in the rate of satellite launches. To keep up with the technology, it is imperative that the launch cost must be made affordable, especially in developing and underdeveloped countries. Launch cost is directly affected by the launch vehicle’s aerodynamic performance. Pegasus-XL SLV (Satellite Launch Vehicle) has been serving as a commercial SLV for the last 26 years, commencing its commercial flight operation from the six operational sites all around the US and Europe, and the Marshal Islands. Aerodynamic heating and drag contribute largely to Pegasus’s flight performance. The objective of this study is to reduce the aerodynamic heating and drag on Pegasus’s body significantly for supersonic and hypersonic flight regimes. Aerodynamic data for Pegasus’s first flight has been validated through CFD (Computational Fluid Dynamics), and then drag and aerodynamic heating is reduced by using a combination of a forward-facing cylindrical spike and a conical aero-disk at the actual operational flight conditions. CFD analysis using ANSYS fluent will be carried out for Mach no. ranges from 0.83 to 7.8, and AoA (Angle of Attack) ranges from -4 to +24 degrees for both simple and spiked-configuration, and then the comparison will be drawn using a variety of graphs and contours. Expected drag reduction for supersonic flight is to be around 15% to 25%, and for hypersonic flight is to be around 30% to 50%, especially for AoA < 15⁰. A 5% to 10% reduction in aerodynamic heating is expected to be achieved for hypersonic regions. In conclusion, the aerodynamic performance of air-launched Pegasus-XL SLV can be further enhanced, leading to its optimal fuel usage to achieve a more economical orbital flight.

Keywords: aerodynamics, pegasus-XL, drag reduction, aerodynamic heating, satellite launch vehicle, SLV, spike, aero-disk

Procedia PDF Downloads 80