Search results for: computational modelling

Authors: Smaran Manchala

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Homomorphic Encryption (HE) enables computations on encrypted data without requiring decryption, mitigating data vulnerability during processing. Usable Fully Homomorphic Encryption (FHE) could revolutionize secure data operations across cloud computing, AI training, and healthcare, providing both privacy and functionality, however, the computational inefficiency of schemes like Cheon-Kim-Kim-Song (CKKS) hinders their widespread practical use. This study focuses on optimizing CKKS for faster matrix operations through the implementation of vector computation parallelization and GPU acceleration. The variable effects of vector parallelization on GPUs were explored, recognizing that while parallelization typically accelerates operations, it could introduce overhead that results in slower runtimes, especially in smaller, less computationally demanding operations. To assess performance, two neural network models, MLPN and CNN—were tested on the MNIST dataset using both ARM and x86-64 architectures, with CNN chosen for its higher computational demands. Each test was repeated 1,000 times, and outliers were removed via Z-score analysis to measure the effect of vector parallelization on CKKS performance. Model accuracy was also evaluated under CKKS encryption to ensure optimizations did not compromise results. According to the results of the trail runs, applying vector parallelization had a 2.63X efficiency increase overall with a 1.83X performance increase for x86-64 over ARM architecture. Overall, these results suggest that the application of vector parallelization in tandem with GPU acceleration significantly improves the efficiency of CKKS even while accounting for vector parallelization overhead, providing impact in future zero trust operations.

Keywords: CKKS scheme, runtime efficiency, fully homomorphic encryption (FHE), GPU acceleration, vector parallelization

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Authors: Moustapha H. Ibrahim, Dahir Abdourahman

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This document presents a short-circuit fault detection process in a photovoltaic (PV) system. The proposed method is developed in MATLAB/Simulink. It determines whatever the size of the installation number of the short circuit module. The proposed algorithm indicates the presence or absence of an abnormality on the power of the PV system through measures of hourly global irradiation, power output, and ambient temperature. In case a fault is detected, it displays the number of modules in a short circuit. This fault detection method has been successfully tested on two different PV installations.

Keywords: PV system, short-circuit, fault detection, modelling, MATLAB-Simulink

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Authors: Ali K. M. Al-Nasrawi, Uday A. Al-Hamdany, Sarah M. Hamylton, Brian G. Jones, Yasir M. Alyazichi

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Surface elevation dynamics have always responded to disturbance regimes. Creating Digital Elevation Models (DEMs) to detect surface dynamics has led to the development of several methods, devices and data clouds. DEMs can provide accurate and quick results with cost efficiency, in comparison to the inherited geomatics survey techniques. Nowadays, remote sensing datasets have become a primary source to create DEMs, including LiDAR point clouds with GIS analytic tools. However, these data need to be tested for error detection and correction. This paper evaluates various DEMs from different data sources over time for Apple Orchard Island, a coastal site in southeastern Australia, in order to detect surface dynamics. Subsequently, 30 chosen locations were examined in the field to test the error of the DEMs surface detection using high resolution global positioning systems (GPSs). Results show significant surface elevation changes on Apple Orchard Island. Accretion occurred on most of the island while surface elevation loss due to erosion is limited to the northern and southern parts. Concurrently, the projected differential correction and validation method aimed to identify errors in the dataset. The resultant DEMs demonstrated a small error ratio (≤ 3%) from the gathered datasets when compared with the fieldwork survey using RTK-GPS. As modern modelling approaches need to become more effective and accurate, applying several tools to create different DEMs on a multi-temporal scale would allow easy predictions in time-cost-frames with more comprehensive coverage and greater accuracy. With a DEM technique for the eco-geomorphic context, such insights about the ecosystem dynamic detection, at such a coastal intertidal system, would be valuable to assess the accuracy of the predicted eco-geomorphic risk for the conservation management sustainability. Demonstrating this framework to evaluate the historical and current anthropogenic and environmental stressors on coastal surface elevation dynamism could be profitably applied worldwide.

Keywords: DEMs, eco-geomorphic-dynamic processes, geospatial Information Science, remote sensing, surface elevation changes,

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Authors: Hammad Majeed, Hanna Knuutila, Magne Hilestad, Hallvard Svendsen

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Formation of aerosols can cause serious complications in industrial exhaust gas CO2 capture processes. SO3 present in the flue gas can cause aerosol formation in an absorption based capture process. Small mist droplets and fog formed can normally not be removed in conventional demisting equipment because their submicron size allows the particles or droplets to follow the gas flow. As a consequence of this aerosol based emissions in the order of grams per Nm3 have been identified from PCCC plants. In absorption processes aerosols are generated by spontaneous condensation or desublimation processes in supersaturated gas phases. Undesired aerosol development may lead to amine emissions many times larger than what would be encountered in a mist free gas phase in PCCC development. It is thus of crucial importance to understand the formation and build-up of these aerosols in order to mitigate the problem.Rigorous modelling of aerosol dynamics leads to a system of partial differential equations. In order to understand mechanics of a particle entering an absorber an implementation of the model is created in Matlab. The model predicts the droplet size, the droplet internal variable profiles and the mass transfer fluxes as function of position in the absorber. The Matlab model is based on a subclass method of weighted residuals for boundary value problems named, orthogonal collocation method. The model comprises a set of mass transfer equations for transferring components and the essential diffusion reaction equations to describe the droplet internal profiles for all relevant constituents. Also included is heat transfer across the interface and inside the droplet. This paper presents results describing the basic simulation tool for the characterization of aerosols formed in CO2 absorption columns and gives examples as to how various entering droplets grow or shrink through an absorber and how their composition changes with respect to time. Below are given some preliminary simulation results for an aerosol droplet composition and temperature profiles. Results: As an example a droplet of initial size of 3 microns, initially containing a 5M MEA, solution is exposed to an atmosphere free of MEA. Composition of the gas phase and temperature is changing with respect to time throughout the absorber.

Keywords: amine solvents, emissions, global climate change, simulation and modelling, aerosol generation

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Authors: Retno A. Vinarti, Lucy M. Hederman

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This research describes a knowledge model for a system which give personalized alert to users about infectious disease risks in the context of weather, location and time. The knowledge model is based on established epidemiological concepts augmented by information gleaned from infection-related data repositories. The existing disease risk prediction research has more focuses on utilizing raw historical data and yield seasonal patterns of infectious disease risk emergence. This research incorporates both data and epidemiological concepts gathered from Atlas of Human Infectious Disease (AHID) and Centre of Disease Control (CDC) as basic reasoning of infectious disease risk prediction. Using CommonKADS methodology, the disease risk prediction task is an assignment synthetic task, starting from knowledge identification through specification, refinement to implementation. First, knowledge is gathered from AHID primarily from the epidemiology and risk group chapters for each infectious disease. The result of this stage is five major elements (Person, Infectious Disease, Weather, Location and Time) and their properties. At the knowledge specification stage, the initial tree model of each element and detailed relationships are produced. This research also includes a validation step as part of knowledge refinement: on the basis that the best model is formed using the most common features, Frequency-based Selection (FBS) is applied. The portion of the Infectious Disease risk model relating to Person comes out strongest, with Location next, and Weather weaker. For Person attribute, Age is the strongest, Activity and Habits are moderate, and Blood type is weakest. At the Location attribute, General category (e.g. continents, region, country, and island) results much stronger than Specific category (i.e. terrain feature). For Weather attribute, Less Precise category (i.e. season) comes out stronger than Precise category (i.e. exact temperature or humidity interval). However, given that some infectious diseases are significantly more serious than others, a frequency based metric may not be appropriate. Future work will incorporate epidemiological measurements of disease seriousness (e.g. odds ratio, hazard ratio and fatality rate) into the validation metrics. This research is limited to modelling existing knowledge about epidemiology and chain of infection concepts. Further step, verification in knowledge refinement stage, might cause some minor changes on the shape of tree.

Keywords: epidemiology, knowledge modelling, infectious disease, prediction, risk

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Authors: Lukas Reznak, Maria Reznakova

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Recession of an economy has a profound negative effect on all involved stakeholders. It follows that timely prediction of recessions has been of utmost interest both in the theoretical research and in practical macroeconomic modelling. Current mainstream of recession prediction is based on standard OLS models of continuous GDP using macroeconomic data. This approach is not suitable for two reasons: the standard continuous models are proving to be obsolete and the macroeconomic data are unreliable, often revised many years retroactively. The aim of the paper is to explore a different branch of recession forecasting research theory and verify the findings on real data of the Czech Republic and Germany. In the paper, the authors present a family of discrete choice probit models with parameters estimated by the method of maximum likelihood. In the basic form, the probits model a univariate series of recessions and expansions in the economic cycle for a given country. The majority of the paper deals with more complex model structures, namely dynamic and bivariate extensions. The dynamic structure models the autoregressive nature of recessions, taking into consideration previous economic activity to predict the development in subsequent periods. Bivariate extensions utilize information from a foreign economy by incorporating correlation of error terms and thus modelling the dependencies of the two countries. Bivariate models predict a bivariate time series of economic states in both economies and thus enhance the predictive performance. A vital enabler of timely and successful recession forecasting are reliable and readily available data. Leading indicators, namely the yield curve and the stock market indices, represent an ideal data base, as the pieces of information is available in advance and do not undergo any retroactive revisions. As importantly, the combination of yield curve and stock market indices reflect a range of macroeconomic and financial market investors’ trends which influence the economic cycle. These theoretical approaches are applied on real data of Czech Republic and Germany. Two models for each country were identified – each for in-sample and out-of-sample predictive purposes. All four followed a bivariate structure, while three contained a dynamic component.

Keywords: bivariate probit, leading indicators, recession forecasting, Czech Republic, Germany

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Authors: Valeria Bondarenko

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We proposed algorithms for: estimation of parameters fBm (volatility and Hurst exponent) and for the approximation of random time series by functional of fBm. We proved the consistency of the estimators, which constitute the above algorithms, and proved the optimal forecast of approximated time series. The adequacy of estimation algorithms, approximation, and forecasting is proved by numerical experiment. During the process of creating software, the system has been created, which is displayed by the hierarchical structure. The comparative analysis of proposed algorithms with the other methods gives evidence of the advantage of approximation method. The results can be used to develop methods for the analysis and modeling of time series describing the economic, physical, biological and other processes.

Keywords: mathematical model, random process, Wiener process, fractional Brownian motion

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Authors: U. Chattaraj, K. Dhusiya, M. Raviteja

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Perception reaction time of drivers is an outcome of human thought process, which is vague and approximate in nature and also varies from driver to driver. So, in this study a fuzzy logic based model for prediction of the same has been presented, which seems suitable. The control factors, like, age, experience, intensity of driving of the driver, speed of the vehicle and distance of stimulus have been considered as premise variables in the model, in which the perception reaction time is the consequence variable. Results show that the model is able to explain the impacts of the control factors on perception reaction time properly.

Keywords: driver, fuzzy logic, perception reaction time, premise variable

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Authors: Vemula Amalakrishna, S. Pushpavanam

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Change and motion characterize and persistently reshape the world around us, on scales from molecular to global. The subtle interplay between change (Reaction) and motion (Diffusion) gives rise to an astonishing intricate spatial or temporal pattern. These pattern formation in nature has been intellectually appealing for many scientists since antiquity. Periodic precipitation patterns, also known as Liesegang patterns (LP), are one of the stimulating examples of such self-assembling reaction-diffusion (RD) systems. LP formation has a great potential in micro and nanotechnology. So far, the research on LPs has been concentrated mostly on how these patterns are forming, retrieving information to build a universal mathematical model for them. Researchers have developed various theoretical models to comprehensively construct the geometrical diversity of LPs. To the best of our knowledge, simulation studies of LPs assume an arbitrary value of RD parameters to explain experimental observation qualitatively. In this work, existing models were studied to understand the mechanism behind this phenomenon and challenges pertaining to models were understood and explained. These models are not computationally effective due to the presence of discontinuous precipitation rate in RD equations. To overcome the computational challenges, smoothened Heaviside functions have been introduced, which downsizes the computational time as well. Experiments were performed using a conventional LP system (AgNO₃-K₂Cr₂O₇) to understand the effects of different gels and temperatures on formed LPs. The model is extended for real parameter values to compare the simulated results with experimental data for both 1-D (Cartesian test tubes) and 2-D(cylindrical and Petri dish).

Keywords: reaction-diffusion, spatio-temporal patterns, nucleation and growth, supersaturation

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Authors: A. Brahma

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Analysis of deferred deformations of concrete under sustained load shows that the creep has a leading role on deferred deformations of concrete structures. Knowledge of the creep characteristics of concrete is a Necessary starting point in the design of structures for crack control. Such knowledge will enable the designer to estimate the probable deformation in pre-stressed concrete or reinforced and the appropriate steps can be taken in design to accommodate this movement. In this study, we propose a prediction model that involves the acting principal parameters on the deferred behaviour of concrete structures. For the estimation of the model parameters Levenberg-Marquardt method has proven very satisfactory. A confrontation between the experimental results and the predictions of models designed shows that it is well suited to describe the evolution of the creep of concrete structures.

Keywords: concrete structure, creep, modelling, prediction

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Authors: M. Sharif Imam Ibne Amir, Mohammad Masud Kamal Khan, Mohammad Golam Rasul, Raj H. Sharma, Fatema Akram

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Future flood can be predicted using the probable maximum flood (PMF). PMF is calculated using the historical discharge or rainfall data considering the other climatic parameter stationary. However, climate is changing globally and the key climatic variables are temperature, evaporation, rainfall and sea level rise (SLR). To develop scenarios to a basin or catchment scale these important climatic variables should be considered. Nowadays scenario based on climatic variables is more suitable than PMF. Six scenarios were developed for a large Fitzroy basin and presented in this paper.

Keywords: climate change, rainfall, potential evaporation, scenario, sea level rise (SLR), sub-catchment

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Authors: Ivana Scidà, Francesco Alotto, Anna Osello

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Due to the importance given today to innovation, the education sector is evolving thanks digital technologies. Virtual Reality (VR) can be a potential teaching tool offering many advantages in the field of training and education, as it allows to acquire theoretical knowledge and practical skills using an immersive experience in less time than the traditional educational process. These assumptions allow to lay the foundations for a new educational environment, involving and stimulating for students. Starting from the objective of strengthening the innovative teaching offer and the learning processes, the case study of the research concerns the digitalization of MastrLAB, High Quality Laboratory (HQL) belonging to the Department of Structural, Building and Geotechnical Engineering (DISEG) of the Polytechnic of Turin, a center specialized in experimental mechanical tests on traditional and innovative building materials and on the structures made with them. The MastrLAB-VR has been developed, a revolutionary innovative training tool designed with the aim of educating the class in total safety on the techniques of use of machinery, thus reducing the dangers arising from the performance of potentially dangerous activities. The virtual laboratory, dedicated to the students of the Building and Civil Engineering Courses of the Polytechnic of Turin, has been projected to simulate in an absolutely realistic way the experimental approach to the structural tests foreseen in their courses of study: from the tensile tests to the relaxation tests, from the steel qualification tests to the resilience tests on elements at environmental conditions or at characterizing temperatures. The research work proposes a methodology for the virtualization of technical laboratories through the application of Building Information Modelling (BIM), starting from the creation of a digital model. The process includes the creation of an independent application, which with Oculus Rift technology will allow the user to explore the environment and interact with objects through the use of joypads. The application has been tested in prototype way on volunteers, obtaining results related to the acquisition of the educational notions exposed in the experience through a virtual quiz with multiple answers, achieving an overall evaluation report. The results have shown that MastrLAB-VR is suitable for both beginners and experts and will be adopted experimentally for other laboratories of the University departments.

Keywords: building information modelling, digital learning, education, virtual laboratory, virtual reality

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Authors: Revanth Rallapalli

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Manually dispensing solids and powders can be difficult as it requires gradually pour and check the amount on the scale to be dispensed. Current systems are manual and non-continuous in nature and are user-dependent and difficult to control powder dispensation. Recurrent dosing of powdered medicines in precise amounts quickly and accurately has been an all-time challenge. Various new powder dispensing mechanisms are being designed to overcome these challenges. A battery-operated screw conveyor mechanism is being innovated to overcome the above problems faced. These inventions are numerically evaluated at the concept development level by employing Computational Fluid Dynamics (CFD) of gas-solids multiphase flow systems. CFD has been very helpful in development of such devices saving time and money by reducing the number of prototypes and testing. Furthermore, this paper describes a simulation of powder dispensation from the trocar’s end by considering the powder as secondary flow in air, is simulated by using the technique called Dense Discrete Phase Model incorporated with Kinetic Theory of Granular Flow (DDPM-KTGF). By considering the volume fraction of powder as 50%, the transportation of powder from the inlet side to trocar’s end side is done by rotation of the screw conveyor. Thus, the performance is calculated for a 1-sec time frame in an unsteady computation manner. This methodology will help designers in developing design concepts to improve the dispensation and also at the effective area within a quick turnaround time frame.

Keywords: DDPM-KTGF, gas-solids multiphase flow, screw conveyor, Unsteady

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Authors: Xi Gu, Guan Heng Yeoh, Victoria Timchenko

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In the current work, a three-dimensional geometry of a 75% stenosed blood vessel is analysed. Large eddy simulation (LES) with the help of a dynamic subgrid scale Smagorinsky model is applied to model the turbulent pulsatile flow. The geometry, the transmural pressure and the properties of the blood and the elastic boundary were based on clinical measurement data. For the flexible wall model, a thin solid region is constructed around the 75% stenosed blood vessel. The deformation of this solid region was modelled as a deforming boundary to reduce the computational cost of the solid model. Fluid-structure interaction is realised via a two-way coupling between the blood flow modelled via LES and the deforming vessel. The information of the flow pressure and the wall motion was exchanged continually during the cycle by an arbitrary lagrangian-eulerian method. The boundary condition of current time step depended on previous solutions. The fluctuation of the velocity in the post-stenotic region was analysed in the study. The axial velocity at normalised position Z=0.5 shows a negative value near the vessel wall. The displacement of the elastic boundary was concerned in this study. In particular, the wall displacement at the systole and the diastole were compared. The negative displacement at the stenosis indicates a collapse at the maximum velocity and the deceleration phase.

Keywords: Large Eddy Simulation, Fluid Structural Interaction, constricted artery, Computational Fluid Dynamics

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Authors: Sania K. Rao, Feza T. Azmi

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The purpose of the present study is to explore the relationship between time management behaviour and job satisfaction of academicians of higher education institutes in India. The analyses of this study were carried out with AMOS (version 20.0); and Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) were conducted. The factor analysis and findings show that perceived control of time serves as the partial mediating factor to have a significant and positive influence on job satisfaction. Further, at the end, a number of suggestions to improve one’s time management behaviour were provided.

Keywords: time management behaviour, job satisfaction, higher education, India, mediation analysis

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN.

Keywords: structural health monitoring, dynamic response, artificial neural network, radial basis function network, genetic algorithm

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Authors: Muhammad Khairi bin Sulaiman

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The utilization of Building Information Modelling (BIM) in the construction industry has provided an opportunity for designers in the Architecture, Engineering and Construction (AEC) industry to proceed from the conventional method of using manual drafting to a way that creates alternative designs quickly, produces more accurate, reliable and consistent outputs. By using BIM Software, designers can create digital content that manipulates the use of data using the parametric model of BIM. With BIM software, more alternative designs can be created quickly and design problems can be explored further to produce a better design faster than conventional design methods. Generally, BIM is used as a documentation mechanism and has not been fully explored and utilised its capabilities as a design tool. Relative to the current issue, Modular Coordination (MC) design as a sustainable design practice is encouraged since MC design will reduce material wastage through standard dimensioning, pre-fabrication, repetitive, modular construction and components. However, MC design involves a complex process of rules and dimensions. Therefore, a tool is needed to make this process easier. Since the parameters in BIM can easily be manipulated to follow MC rules and dimensioning, thus, the integration of BIM software with MC design is proposed for architects during the design stage. With this tool, there will be an improvement in acceptance and practice in the application of MC design effectively. Consequently, this study will analyse and explore the function and customization of BIM objects and the capability of BIM software to expedite the application of MC design during the design stage for architects. With this application, architects will be able to create building models and locate objects within reference modular grids that adhere to MC rules and dimensions. The parametric modeling capabilities of BIM will also act as a visual tool that will further enhance the automation of the 3-Dimensional space planning modeling process. (Method) The study will first analyze and explore the parametric modeling capabilities of rule-based BIM objects, which eventually customize a reference grid within the rules and dimensioning of MC. Eventually, the approach will further enhance the architect's overall design process and enable architects to automate complex modeling, which was nearly impossible before. A prototype using a residential quarter will be modeled. A set of reference grids guided by specific MC rules and dimensions will be used to develop a variety of space planning and configuration. With the use of the design, the tool will expedite the design process and encourage the use of MC Design in the construction industry.

Keywords: building information modeling, modular coordination, space planning, customization, BIM application, MC space planning

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Authors: Sidi M. A. Ghaly, Mohammad O. Khan, Mohammed Shalaby, Khaled A. Al-Snaie

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Electrical capacitance tomography (ECT) is a valuable, non-invasive technique used to monitor multiphase flow processes, especially within industrial pipelines. This study focuses on the design, testing, and performance comparison of ECT sensors configured with 8, 12, and 16 electrodes, aiming to evaluate their effectiveness in imaging accuracy, resolution, and sensitivity. Each sensor configuration was designed to capture the spatial permittivity distribution within a pipeline cross-section, enabling visualization of phase distribution and flow characteristics such as oil and water interactions. The sensor designs were implemented and tested in closed pipes to assess their response to varying flow regimes. Capacitance data collected from each electrode configuration were reconstructed into cross-sectional images, enabling a comparison of image resolution, noise levels, and computational demands. Results indicate that the 16-electrode configuration yields higher image resolution and sensitivity to phase boundaries compared to the 8- and 12-electrode setups, making it more suitable for complex flow visualization. However, the 8 and 12-electrode sensors demonstrated advantages in processing speed and lower computational requirements. This comparative analysis provides critical insights into optimizing ECT sensor design based on specific industrial requirements, from high-resolution imaging to real-time monitoring needs.

Keywords: capacitance tomography, modeling, simulation, electrode, permittivity, fluid dynamics, imaging sensitivity measurement

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Authors: Nkemjika Chinenye-Kanu, Mamdud Hossain, Ghazi Droubi

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Prediction of multiphase flow induced forces, void fraction and pressure is crucial at both design and operating stages of practical energy and process pipe systems. In this study, transient numerical simulations of upward slug-churn flow through a vertical 90-degree elbow have been conducted. The volume of fluid (VOF) method was used to model the two-phase flows while the K-epsilon Reynolds-Averaged Navier-Stokes (RANS) equations were used to model turbulence in the flows. The simulation results were validated using experimental results. Void fraction signal, peak frequency and maximum magnitude of void fraction fluctuation of the slug-churn flow validation case studies compared well with experimental results. The x and y direction force fluctuation signals at the elbow control volume were obtained by carrying out force balance calculations using the directly extracted time domain signals of flow properties through the control volume in the numerical simulation. The computed force signal compared well with experiment for the slug and churn flow validation case studies. Hence, the present numerical simulation technique was able to predict the behaviours of the one-way flow induced forces and void fraction fluctuations.

Keywords: computational fluid dynamics, flow induced vibration, slug-churn flow, void fraction and force fluctuation

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Authors: Hossein Abbasi

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The stability is an important topic to plan and manage the energy in the microgrids as the same as the conventional power systems. The voltage and frequency stability is one of the most important issues recently studied in microgrids. The objectives of this paper are the modelling and designing of the components and optimal controllers for the voltage and frequency control of the AC/DC hybrid microgrid under the different disturbances. Since the PI controllers have the advantages of simple structure and easy implementation, so they are designed and modeled in this paper. The harmony search (HS) algorithm is used to optimize the controllers’ parameters. According to the achieved results, the PI controllers have a good performance in voltage and frequency control of the microgrid.

Keywords: frequency control, HS algorithm, microgrid, PI controller, voltage control

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Authors: Malihe Aliasgari, Yousef Nejatbakhsh

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The era of Big Data and the immensity of real-life datasets compels computation tasks to be performed in a distributed fashion, where the data is dispersed among many servers that operate in parallel. However, massive parallelization leads to computational bottlenecks due to faulty servers and stragglers. Stragglers refer to a few slow or delay-prone processors that can bottleneck the entire computation because one has to wait for all the parallel nodes to finish. The problem of straggling processors, has been well studied in the context of distributed computing. Recently, it has been pointed out that, for the important case of linear functions, it is possible to improve over repetition strategies in terms of the tradeoff between performance and latency by carrying out linear precoding of the data prior to processing. The key idea is that, by employing suitable linear codes operating over fractions of the original data, a function may be completed as soon as enough number of processors, depending on the minimum distance of the code, have completed their operations. The problem of matrix-matrix multiplication in the presence of practically big sized of data sets faced with computational and memory related difficulties, which makes such operations are carried out using distributed computing platforms. In this work, we study the problem of distributed matrix-matrix multiplication W = XY under storage constraints, i.e., when each server is allowed to store a fixed fraction of each of the matrices X and Y, which is a fundamental building of many science and engineering fields such as machine learning, image and signal processing, wireless communication, optimization. Non-secure and secure matrix multiplication are studied. We want to study the setup, in which the identity of the matrix of interest should be kept private from the workers and then obtain the recovery threshold of the colluding model, that is, the number of workers that need to complete their task before the master server can recover the product W. The problem of secure and private distributed matrix multiplication W = XY which the matrix X is confidential, while matrix Y is selected in a private manner from a library of public matrices. We present the best currently known trade-off between communication load and recovery threshold. On the other words, we design an achievable PSGPD scheme for any arbitrary privacy level by trivially concatenating a robust PIR scheme for arbitrary colluding workers and private databases and the proposed SGPD code that provides a smaller computational complexity at the workers.

Keywords: coded distributed computation, private information retrieval, secret sharing, stragglers

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Authors: Dhruvit S. Berawala, Jann R. Ursin, Obrad Slijepcevic

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Shale gas is one of the most rapidly growing forms of natural gas. Unconventional natural gas deposits are difficult to characterize overall, but in general are often lower in resource concentration and dispersed over large areas. Moreover, gas is densely packed into the matrix through adsorption which accounts for large volume of gas reserves. Gas production from tight shale deposits are made possible by extensive and deep well fracturing which contacts large fractions of the formation. The conventional reservoir modelling and production forecasting methods, which rely on fluid-flow processes dominated by viscous forces, have proved to be very pessimistic and inaccurate. This paper presents a new approach to forecast shale gas production by detailed modeling of gas desorption, diffusion and non-linear flow mechanisms in combination with statistical representation of these processes. The representation of the model involves a cube as a porous media where free gas is present and a sphere (SiC: Sphere in Cube model) inside it where gas is adsorbed on to the kerogen or organic matter. Further, the sphere is considered consisting of many layers of adsorbed gas in an onion-like structure. With pressure decline, the gas desorbs first from the outer most layer of sphere causing decrease in its molecular concentration. The new available surface area and change in concentration triggers the diffusion of gas from kerogen. The process continues until all the gas present internally diffuses out of the kerogen, gets adsorbs onto available surface area and then desorbs into the nanopores and micro-fractures in the cube. Each SiC idealizes a gas pathway and is characterized by sphere diameter and length of the cube. The diameter allows to model gas storage, diffusion and desorption; the cube length takes into account the pathway for flow in nanopores and micro-fractures. Many of these representative but general cells of the reservoir are put together and linked to a well or hydraulic fracture. The paper quantitatively describes these processes as well as clarifies the geological conditions under which a successful shale gas production could be expected. A numerical model has been derived which is then compiled on FORTRAN to develop a simulator for the production of shale gas by considering the spheres as a source term in each of the grid blocks. By applying SiC to field data, we demonstrate that the model provides an effective way to quickly access gas production rates from shale formations. We also examine the effect of model input properties on gas production.

Keywords: adsorption, diffusion, non-linear flow, shale gas production

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Authors: Xiyang Li, Qi Yu, Lun Zhang

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In the face of natural disasters and other emergency situations, determining the optimal location of rescue centers is crucial for improving rescue efficiency and minimizing impact on affected populations. This paper proposes a method that integrates genetic algorithms (GA) and backpropagation neural networks (BPNN) to address the site selection optimization problem for emergency rescue centers. We utilize BPNN to accurately estimate the cost of delivering supplies from rescue centers to each temporary camp. Moreover, a genetic algorithm with a special partially matched crossover (PMX) strategy is employed to ensure that the number of temporary camps assigned to each rescue center adheres to predetermined limits. Using the population distribution data during the 2022 epidemic in Jiading District, Shanghai, as an experimental case, this paper verifies the effectiveness of the proposed method. The experimental results demonstrate that the BPNN-GA method proposed in this study outperforms existing algorithms in terms of computational efficiency and optimization performance. Especially considering the requirements for computational resources and response time in emergency situations, the proposed method shows its ability to achieve rapid convergence and optimal performance in the early and mid-stages. Future research could explore incorporating more real-world conditions and variables into the model to further improve its accuracy and applicability.

Keywords: emergency rescue centers, genetic algorithms, back-propagation neural networks, site selection optimization

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Authors: Divya Vats, Sanjay Mahajani

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The clarification of sugarcane juice plays a pivotal role in the production of non-centrifugal sugar (NCS), profoundly influencing the quality of the final NCS product. In this study, we have investigated the kinetics and mathematical modeling of the batch clarification process. The turbidity of the clarified cane juice (NTU) emerges as the determinant of the end product’s color. Moreover, this parameter underscores the significance of considering other variables as performance indicators for accessing the efficacy of the clarification process. Temperature-controlled experiments were meticulously conducted in a laboratory-scale batch mode. The primary objective was to discern the essential and optimized parameters crucial for augmenting the clarity of cane juice. Additionally, we explored the impact of pH and flocculant loading on the kinetics. Particle Image Velocimetry (PIV) is employed to comprehend the particle-particle and fluid-particle interaction. This technique facilitated a comprehensive understanding, paving the way for the subsequent multiphase computational fluid dynamics (CFD) simulations using the Eulerian-Lagrangian approach in the Ansys fluent. Impressively, these simulations accurately replicated comparable velocity profiles. The final mechanism of this study helps to make a mathematical model and presents a valuable framework for transitioning from the traditional batch process to a continuous process. The ultimate aim is to attain heightened productivity and unwavering consistency in product quality.

Keywords: non-centrifugal sugar, particle image velocimetry, computational fluid dynamics, mathematical modeling, turbidity

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Authors: Mohamad Molavi Nojumi, Xiaodong Wang

Abstract:

In this research fracture behaviour of linear elastic isotropic functionally graded materials (FGMs) are investigated using modified finite element method (FEM). FGMs are advantageous because they enhance the bonding strength of two incompatible materials, and reduce the residual stress and thermal stress. Ceramic/metals are a main type of FGMs. Ceramic materials are brittle. So, there is high possibility of crack existence during fabrication or in-service loading. In addition, damage analysis is necessary for a safe and efficient design. FEM is a strong numerical tool for analyzing complicated problems. Thus, FEM is used to investigate the fracture behaviour of FGMs. Here an accurate 9-node biquadratic quadrilateral graded element is proposed in which the influence of the variation of material properties is considered at the element level. The stiffness matrix of graded elements is obtained using the principle of minimum potential energy. The implementation of graded elements prevents the forced sudden jump of material properties in traditional finite elements for modelling FGMs. Numerical results are verified with existing solutions. Different numerical simulations are carried out to model stationary crack problems in nonhomogeneous plates. In these simulations, material variation is supposed to happen in directions perpendicular and parallel to the crack line. Two special linear and exponential functions have been utilized to model the material gradient as they are mostly discussed in literature. Also, various sizes of the crack length are considered. A major difference in the fracture behaviour of FGMs and homogeneous materials is related to the break of material symmetry. For example, when the material gradation direction is normal to the crack line, even under applying the mode I loading there exists coupled modes I and II of fracture which originates from the induced shear in the model. Therefore, the necessity of the proper modelling of the material variation should be considered in capturing the fracture behaviour of FGMs specially, when the material gradient index is high. Fracture properties such as mode I and mode II stress intensity factors (SIFs), energy release rates, and field variables near the crack tip are investigated and compared with results obtained using conventional homogeneous elements. It is revealed that graded elements provide higher accuracy with less effort in comparison with conventional homogeneous elements.

Keywords: finite element, fracture mechanics, functionally graded materials, graded element

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Authors: Alex Soares Castro, Matheus Manoel Teles de Menezes, Larissa Silva de Azevedo, Ana Carolina Caleffi Patelli, Osmair Vital de Oliveira, Aline Thais Bruni, Marcelo Firmino de Oliveira

Abstract:

Considering the problem of the seizure of illicit drugs, as well as the development of electrochemical sensors using chemically modified electrodes, this work shows the study of the electrochemical activity of cocaine in carbon paste electrode chemically modified with Cu (II) trans 3-MeO salcn complex. In this context, cyclic voltammetry was performed on 0.1 mol.L⁻¹ KCl supporting electrolyte at a scan speed of 100 mV s⁻¹, using an electrochemical cell composed of three electrodes: Ag /AgCl electrode (filled KCl 3 mol.L⁻¹) from Metrohm® (reference electrode); a platinum spiral electrode, as an auxiliary electrode, and a carbon paste electrode chemically modified with Cu (II) trans 3-MeO complex (as working electrode). Two forms of cocaine were analyzed: cocaine hydrochloride (pH 3) and cocaine free base form (pH 8). The PM7 computational method predicted that the hydrochloride form is more stable than the free base form of cocaine, so with cyclic voltammetry, we found electrochemical signal only for cocaine in the form of hydrochloride, with an anodic peak at 1.10 V, with a linearity range between 2 and 20 μmol L⁻¹ had LD and LQ of 2.39 and 7.26x10-5 mol L⁻¹, respectively. The study also proved that cocaine is adsorbed on the surface of the working electrode, where through an irreversible process, where only anode peaks are observed, we have the oxidation of cocaine, which occurs in the hydrophilic region due to the loss of two electrons. The mechanism of this reaction was confirmed by the ab-inito quantum method.

Keywords: ab-initio computational method, analytical method, cocaine, Schiff base complex, voltammetry

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Authors: Fouzia Amireche-Ziar, Ilham Mokbel, Jacques Jose

Abstract:

The vapour pressures of the three binary mixtures: 1, 2- dichloroethane + 1,3-dioxolane, + 1,4-dioxane or + tetrahydropyrane, are carried out at ten temperatures ranging from 273 to 353.15 K. An accurate static device was employed for these measurements. The VLE data were reduced using the Redlich-Kister equation by taking into consideration the vapour pressure non-ideality in terms of the second molar virial coefficient. The experimental data were compared to the results predicted with the DISQUAC and Dortmund UNIFAC group contribution models for the total pressures P and the excess molar Gibbs energies GE.

Keywords: disquac model, dortmund UNIFAC model, excess molar Gibbs energies GE, VLE

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Authors: Daniil Karzanov

Abstract:

This paper studies the effect of quarterly earnings reports on the stock price. The profitability of the stock is modeled by geometric Brownian diffusion and the Constant Elasticity of Variance model. We fit several variations of stochastic differential equations to the pre-and after-report period using the Maximum Likelihood Estimation and Grid Search of parameters method. By examining the change in the model parameters after reports’ publication, the study reveals that the reports have enough evidence to be a structural breakpoint, meaning that all the forecast models exploited are not applicable for forecasting and should be refitted shortly.

Keywords: stock market, earnings reports, financial time series, structural breaks, stochastic differential equations

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Authors: John R Evans, Sook-Ying Ho, Rey Chin

Abstract:

This paper describes the research investigations orientated to the starting and propelling of a solid fuel rocket engine which operates as combined cycle propulsion system using three thrust pulses. The vehicle has been designed to minimise the cost of launching small number of Nano/Cube satellites into low earth orbits (LEO). A technology described in this paper is a ground-based launch propulsion system which starts the rocket vertical motion immediately causing air flow to enter the ramjet’s intake. Current technology has a ramjet operation predicted to be able to start high subsonic speed of 280 m/s using a liquid fuel ramjet (LFRJ). The combined cycle engine configuration is in many ways fundamentally different from the LFRJ. A much lower subsonic start speed is highly desirable since the use of a mortar to obtain the latter speed for rocket means a shorter launcher length can be utilized. This paper examines the means and has some performance calculations, including Computational Fluid Dynamics analysis of air-intake at suitable operational conditions, 3-DOF point mass trajectory analysis of multi-pulse propulsion system (where pulse ignition time and thrust magnitude can be controlled), etc. of getting a combined cycle rocket engine use in a single stage vehicle.

Keywords: combine cycle propulsion system, low earth orbit launch vehicle, computational fluid dynamics analysis, 3dof trajectory analysis

Procedia PDF Downloads 189

Authors: S. Alireza Mirghasemi, Javad Parvizi, Narges R. Gabaran, Shervin Rashidinia, Mahdi M. Bijanabadi, Dariush G. Savadkoohi

Abstract:

Background: The anterior cruciate ligament is one of the most frequent ligament to be disrupted. Surgical reconstruction of the anterior cruciate ligament is a common practice to treat the disability or chronic instability of the knee. Several factors associated with success or failure of the ACL reconstruction including preoperative laxity of the knee, selection of the graft material, surgical technique, graft tension, and postoperative rehabilitation. We aimed to examine the biomechanical properties of any graft type and initial graft tensioning during ACL reconstruction using 3-dimensional computational finite element simulation. Methods: In this paper, 3-dimensional model of the knee was constructed to investigate the effect of graft tensioning on the knee joint biomechanics. Four different grafts were compared: 1) Bone-patellar tendon-bone graft (BPTB) 2) Hamstring tendon 3) BPTB and a band of gracilis4) Hamstring and a band of gracilis. The initial graft tension was set as “0, 20, 40, or 60N”. The anterior loading was set to 134 N. Findings: The resulting stress pattern and deflection in any of these models were compared to that of the intact knee. The obtained results showed that the combination of a band of gracilis with the former graft (BPTB or Hamstring) increases the structural stiffness of the knee. Conclusion: Required pretension during surgery decreases significantly by adding a band of gracilis to the proper graft.

Keywords: ACL reconstruction, deflection, finite element simulation, stress pattern

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