Search results for: prediction modelling

Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Use of speed prediction models sometimes appears as a feasible alternative to laborious field measurement particularly, in case when field data cannot fulfill designer’s requirements. However, developing speed models is a challenging task specifically in the context of developing countries like India where vehicles with diverse static and dynamic characteristics use the same right of way without any segregation. Here the traffic composition plays a significant role in determining the vehicular speed. The present research was carried out to examine the effects of traffic volume and its composition on vehicular speed under mixed traffic conditions. Classified traffic volume and speed data were collected from different geometrically identical six lane divided arterials in New Delhi. Based on these field data, speed prediction models were developed for individual vehicle category adopting Kriging approximation technique, an alternative for commonly used regression. These models are validated with the data set kept aside earlier for validation purpose. The predicted speeds showed a great deal of agreement with the observed values and also the model outperforms all other existing speed models. Finally, the proposed models were utilized to evaluate the effect of traffic volume and its composition on speed.

Keywords: speed, Kriging, arterial, traffic volume

Procedia PDF Downloads 354

Authors: William J. Crowther, Conor Marsh

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Object-based flow simulation allows fast computation of arbitrarily complex aerodynamic models made up of simple objects with limited flow interactions. The proposed approach is universally applicable to objects made from arbitrarily scaled ellipsoid primitives at arbitrary aerodynamic attitude and angular rate. The use of a component-based aerodynamic modelling approach increases efficiency by allowing selective inclusion of different physics models at run-time and allows extensibility through the development of new models. Insight into the numerical stability of the model under first order fixed-time step integration schemes is provided by stability analysis of the drag component. The compute cost of model components and functions is evaluated and compared against numerical benchmarks. Model static outputs are verified against theoretical expectations and dynamic behaviour using falling plate data from the literature. The model is applied to a range of case studies to demonstrate the efficacy of its application in extensibility, ease of use, and low computational cost. Dynamically complex multi-body systems can be implemented in a transparent and efficient manner, and we successfully demonstrate large scenes with hundreds of objects interacting with diverse flow fields.

Keywords: aerodynamics, real-time simulation, low-order model, flight dynamics

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Authors: Aïssa Rezzoug

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This paper is aimed to bring new elements that demonstrate the tide caused the groundwater to rise in the shoreline band, on which the urban areas occurs, especially in the western coastal cities of the Kingdom of Saudi Arabia like Jeddah. The reason for the last events of Jeddah inundation was the groundwater rise in the city coupled at the same time to a strong precipitation event. This paper will illustrate the tide participation in increasing the groundwater level significantly. It shows that the reason for internal groundwater recharge within the urban area is not only the excess of the water supply coming from surrounding areas, due to the human activity, with lack of sufficient and efficient sewage system, but also due to tide effect. The research study follows a quantitative method to assess groundwater level rise risks through many in-situ measurements and mathematical modelling. The proposed approach highlights groundwater level, in the urban areas of the city on the shoreline band, reaching the high tide level without considering any input from precipitation. Despite the small tide in the Red Sea compared to other oceanic coasts, the groundwater level is considerably enhanced by the tide from the seaside and by the freshwater table from the landside of the city. In these conditions, the groundwater level becomes high in the city and prevents the soil to evacuate quickly enough the surface flow caused by the storm event, as it was observed in the last historical flood catastrophe of Jeddah in 2009.

Keywords: flood, groundwater rise, Jeddah, tide

Procedia PDF Downloads 117

Authors: Ali Karrech, Alberto Puccini, Ben Galvin, Davide Galli

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This paper summarises the laboratory tests, numerical models and statistical approach developed to investigate the behaviour of concrete blocks loaded in shear through metallic anchors. This research is proposed to bridge a gap in the state of the art and practice related to anchors loaded against partially confined concrete surfaces. Eight concrete blocks (420 mm x 500 mm x 1000 mm) with 150 and/or 250 deep anchors were tested. The stainless-steel anchors of diameter 16 mm were bonded with HIT-RE 500 V4 injection epoxy resin and were subjected to shear loading against partially supported edges. In addition, finite element models were constructed to validate the laboratory tests and explore the influence of key parameters such as anchor depth, anchor distance from the edge, and compressive strength on the stability of the block. Upon their validation experimentally, the numerical results were used to populate, develop and interpret a systematic parametric study based on the Design of Experiment approach through the Box-Behnken design and Response Surface Methodology. An empirical model has been derived based on this approach, which predicts the load capacity with the desirable intervals of confidence.

Keywords: finite element modelling, design of experiment, response surface methodology, Box-Behnken design, empirical model, interval of confidence, load capacity

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Authors: Lojain Suliman, Xinrong Liu, Xiaohan Zhou

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Since most tunnels are built in crowded metropolitan settings, the excavation process must take place in highly condensed locations, including high-density cities. In this way, the tunnels are typically located close together, which leads to more interaction between the parallel existing tunnels, and this, in turn, leads to more settlement. This research presents an examination of the impact of a large-scale tunnel excavation on two forms of settlement: surface settlement and settlement surrounding the tunnel. Additionally, research has been done on the properties of interactions between two and three parallel tunnels. The settlement has been evaluated using three primary techniques: theoretical modeling, numerical simulation, and data monitoring. Additionally, a parametric investigation on how distance affects the settlement characteristic for parallel tunnels with short distances has been completed. Additionally, it has been observed that the sequence of excavation has an impact on the behavior of settlements. Nevertheless, a comparison of the model test and numerical simulation yields significant agreement in terms of settlement trend and value. Additionally, when compared to the FEM study, the suggested analytical solution exhibits reduced sensitivity in the settlement prediction. For example, the settlement of the small tunnel diameter does not appear clearly on the settlement curve, while it is notable in the FEM analysis. It is advised, however, that additional studies be conducted in the future employing analytical solutions for settlement prediction for parallel tunnels.

Keywords: settlement, FEM, analytical solution, parallel tunnels

Procedia PDF Downloads 42

Authors: F. O. Adeosun, I. O. Orubuloye, O. O. Babalola

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Although caesarean section has greatly improved obstetric care throughout the world, in developing countries there is a great aversion to caesarean section. This study was carried out to examine the rate at which pregnant women with pre-eclampsia choose caesarean section over natural birth. A cross-sectional study was conducted among 500 pre-eclampsia antenatal clients seen at the States University Teaching Hospitals in the last one year. The sample selection was purposive. Information on their educational background, beliefs and attitudes were collected. Data analysis was presented using simple percentages. Out of 500 women studied, 38% favored caesarean section while 62% were against it. About 89% of them understood what caesarean section is, 57.3% of those who understood what caesarean section is will still not choose it as an option. Over 85% of the women believed caesarean section is done for medical reasons. If caesarean section is given as an option for childbirth, 38% would go for it, 29% would try religious intervention, 5.5% would not choose it because of fear, while 27.5% would reject it because they believe it is culturally wrong. Majority of respondents (85%) who favored caesarean delivery are aware of the risk attached to choosing virginal birth but go an extra mile in sourcing funds for a caesarean session while over 64% cannot afford the cost of caesarean delivery. It is therefore pertinent to encourage research in prediction methods and prevention of occurrence, since this would assist patients to plan on how to finance treatment.

Keywords: caesarean section, choice, cost, pre eclampsia, prediction methods

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Authors: Karen C. Olufokunbi, Odetunji A. Odejobi

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The discourse of this paper is the examination of the generation, accumulation and growth of e-waste in a developing country. Images and other data about computer e-waste were collected using a digital camera, 290 copies of questionnaire and three structured interviews using Obafemi Awolowo University (OAU), Ile-Ife, Nigeria environment as a case study. The numerical data were analysed using R data analysis and process tool. Automata-based techniques and Petri net modeling tool were used to design and simulate a computational model for the recovery of saleable materials from e-waste. The R analysis showed that at a 95 percent confidence level, the computer equipment that will be disposed by 2020 will be 417 units. Compared to the 800 units in circulation in 2014, 50 percent of personal computer components will become e-waste. This indicates that personal computer components were in high demand due to their low costs and will be disposed more rapidly when replaced by new computer equipment Also, 57 percent of the respondents discarded their computer e-waste by throwing it into the garbage bin or by dumping it. The simulated model using Coloured Petri net modelling tool for the process showed that the e-waste dynamics is a forward sequential process in the form of a pipeline meaning that an e-waste recovery of saleable materials process occurs in identifiable discrete stages indicating that e-waste will continue to accumulate and grow in volume with time.

Keywords: Coloured Petri net, computational modelling, electronic waste, electronic waste process dynamics

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Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

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A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: artificial neural networks, resilient propagation, solar radiation, time series forecasting

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Authors: Ngoc Trung Ngo, Buddhima Indraratna, Cholachat Rujikiathmakjornr

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For several hundred years, the design of railway tracks has practically remained unchanged. Traditionally, rail tracks are placed on a ballast layer due to several reasons, including economy, rapid drainage, and high load bearing capacity. The primary function of ballast is to distributing dynamic track loads to sub-ballast and subgrade layers, while also providing lateral resistance and allowing for rapid drainage. Upon repeated trainloads, the ballast becomes fouled due to ballast degradation and the intrusion of fines which adversely affects the strength and deformation behaviour of ballast. This paper presents the use of three-dimensional discrete element method (DEM) in studying the shear behaviour of the fouled ballast subjected to direct shear loading. Irregularly shaped particles of ballast were modelled by grouping many spherical balls together in appropriate sizes to simulate representative ballast aggregates. Fouled ballast was modelled by injecting a specified number of miniature spherical particles into the void spaces. The DEM simulation highlights that the peak shear stress of the ballast assembly decreases and the dilation of fouled ballast increases with an increase level of fouling. Additionally, the distributions of contact force chain and particle displacement vectors were captured during shearing progress, explaining the formation of shear band and the evolutions of volumetric change of fouled ballast.

Keywords: railway ballast, coal fouling, discrete element modelling, discrete element method

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Authors: Pinel Sebastien, Bourrin François, De Madron Du Rieu Xavier, Ludwig Wolfgang, Arnau Pedro

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Heavy precipitation constitutes a major meteorological threat in the western Mediterranean. Research has investigated the relationship between the states of the Mediterranean Sea and the atmosphere with the precipitation for short temporal windows. However, at a larger temporal scale, the precursor signals of heavy rainfall in the sea and atmosphere have drawn little attention. Moreover, despite ongoing improvements in numerical weather prediction, the medium-term forecasting of rainfall events remains a difficult task. Here, we aim to investigate the influence of early-spring environmental parameters on the following autumnal heavy precipitations. Hence, we develop a machine learning model to predict extreme autumnal rainfall with a 6-month lead time over the Spanish Catalan coastal area, based on i) the sea pattern (main current-LPC and Sea Surface Temperature-SST) at the mesoscale scale, ii) 4 European weather teleconnection patterns (NAO, WeMo, SCAND, MO) at synoptic scale, and iii) the hydrological regime of the main local river (Rhône River). The accuracy of the developed model classifier is evaluated via statistical analysis based on classification accuracy, logarithmic and confusion matrix by comparing with rainfall estimates from rain gauges and satellite observations (CHIRPS-2.0). Sensitivity tests are carried out by changing the model configuration, such as sea SST, sea LPC, river regime, and synoptic atmosphere configuration. The sensitivity analysis suggests a negligible influence from the hydrological regime, unlike SST, LPC, and specific teleconnection weather patterns. At last, this study illustrates how public datasets can be integrated into a machine learning model for heavy rainfall prediction and can interest local policies for management purposes.

Keywords: extreme hazards, sensitivity analysis, heavy rainfall, machine learning, sea-atmosphere modeling, precipitation forecasting

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Authors: Grienggrai Rajchakit

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As we all know that coronavirus is announced as a pandemic in the world by WHO. It is speeded all over the world with few days of time. To control this spreading, every citizen maintains social distance and self-preventive measures are the best strategies. As of now, many researchers and scientists are continuing their research in finding out the exact vaccine. The machine learning model finds that the coronavirus disease behaves in an exponential manner. To abolish the consequence of this pandemic, an efficient step should be taken to analyze this disease. In this paper, a recurrent neural network model is chosen to predict the number of active cases in a particular state. To make this prediction of active cases, we need a database. The database of COVID-19 is downloaded from the KAGGLE website and is analyzed by applying a recurrent LSTM neural network with univariant features to predict the number of active cases of patients suffering from the corona virus. The downloaded database is divided into training and testing the chosen neural network model. The model is trained with the training data set and tested with a testing dataset to predict the number of active cases in a particular state; here, we have concentrated on Andhra Pradesh state.

Keywords: COVID-19, coronavirus, KAGGLE, LSTM neural network, machine learning

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Authors: Achut Manandhar, Kenneth D. Morton, Peter A. Torrione, Leslie M. Collins

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The current trends in affect recognition research are to consider continuous observations from spontaneous natural interactions in people using multiple feature modalities, and to represent affect in terms of continuous dimensions, incorporate spatio-temporal correlation among affect dimensions, and provide fast affect predictions. These research efforts have been propelled by a growing effort to develop affect recognition system that can be implemented to enable seamless real-time human-computer interaction in a wide variety of applications. Motivated by these desired attributes of an affect recognition system, in this work a multi-dimensional affect prediction approach is proposed by integrating multivariate Relevance Vector Machine (MVRVM) with a recently developed Output-associative Relevance Vector Machine (OARVM) approach. The resulting approach can provide fast continuous affect predictions by jointly modeling the multiple affect dimensions and their correlations. Experiments on the RECOLA database show that the proposed approach performs competitively with the OARVM while providing faster predictions during testing.

Keywords: dimensional affect prediction, output-associative RVM, multivariate regression, fast testing

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Authors: Cristian Păuna

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In the first decades of the 21st century, in the electronic trading environment, algorithmic capital investments became the primary tool to make a profit by speculations in financial markets. A significant number of traders, private or institutional investors are participating in the capital markets every day using automated algorithms. The autonomous trading software is today a considerable part in the business intelligence system of any modern financial activity. The trading decisions and orders are made automatically by computers using different mathematical models. This paper will present one of these models called Price Prediction Line. A mathematical algorithm will be revealed to build a reliable trend line, which is the base for limit conditions and automated investment signals, the core for a computerized investment system. The paper will guide how to apply these tools to generate entry and exit investment signals, limit conditions to build a mathematical filter for the investment opportunities, and the methodology to integrate all of these in automated investment software. The paper will also present trading results obtained for the leading German financial market index with the presented methods to analyze and to compare different automated investment algorithms. It was found that a specific mathematical algorithm can be optimized and integrated into an automated trading system with good and sustained results for the leading German Market. Investment results will be compared in order to qualify the presented model. In conclusion, a 1:6.12 risk was obtained to reward ratio applying the trigonometric method to the DAX Deutscher Aktienindex on 24 months investment. These results are superior to those obtained with other similar models as this paper reveal. The general idea sustained by this paper is that the Price Prediction Line model presented is a reliable capital investment methodology that can be successfully applied to build an automated investment system with excellent results.

Keywords: algorithmic trading, automated trading systems, high-frequency trading, DAX Deutscher Aktienindex

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Authors: B. Almassri, F. Almahmoud, R. Francois

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Near surface mounted reinforcement (NSM) technique is one of the promising techniques used nowadays to strengthen reinforced concrete (RC) structures. In the NSM technique, the Carbon Fibre Reinforced Polymer (CFRP) rods are placed inside pre-cut grooves and are bonded to the concrete with epoxy adhesive. This paper studies the non-classical mode of failure ‘the separation of concrete cover’ according to experimental and numerical FE modelling results. Experimental results and numerical modelling results of a 3D finite element (FE) model using the commercial software Abaqus and 2D FE model FEMIX were obtained on two beams, one corroded (25 years of corrosion procedure) and one control (A1CL3-R and A1T-R) were each repaired in bending using NSM CFRP rod and were then tested up to failure. The results showed that the NSM technique increased the overall capacity of control and corroded beams despite a non-classical mode of failure with separation of the concrete cover occurring in the corroded beam due to damage induced by corrosion. Another FE model used external steel stirrups around the repaired corroded beam A1CL3-R which failed with the separation of concrete cover, this model showed a change in the mode of failure form a non-classical mode of failure by the separation of concrete cover to the same mode of failure of the repaired control beam by the crushing of compressed concrete.

Keywords: corrosion, repair, Reinforced Concrete, FEM, CFRP, FEMIX

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Authors: Ishapathik Das

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The purpose of this article is to find a method of comparing designs for ordinal regression models using quantile dispersion graphs in the presence of linear predictor misspecification. The true relationship between response variable and the corresponding control variables are usually unknown. Experimenter assumes certain form of the linear predictor of the ordinal regression models. The assumed form of the linear predictor may not be correct always. Thus, the maximum likelihood estimates (MLE) of the unknown parameters of the model may be biased due to misspecification of the linear predictor. In this article, the uncertainty in the linear predictor is represented by an unknown function. An algorithm is provided to estimate the unknown function at the design points where observations are available. The unknown function is estimated at all points in the design region using multivariate parametric kriging. The comparison of the designs are based on a scalar valued function of the mean squared error of prediction (MSEP) matrix, which incorporates both variance and bias of the prediction caused by the misspecification in the linear predictor. The designs are compared using quantile dispersion graphs approach. The graphs also visually depict the robustness of the designs on the changes in the parameter values. Numerical examples are presented to illustrate the proposed methodology.

Keywords: model misspecification, multivariate kriging, multivariate logistic link, ordinal response models, quantile dispersion graphs

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Authors: Tang Wei, Yang Xiaofeng, Gui Yewei, Du Yanxia

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Complex lifting entry was selected for precise landing performance during the Mars Science Laboratory entry. This study aims to develop the three-dimensional numerical method for precise computation and the surface panel method for rapid engineering prediction. Detailed flow field analysis for Mars exploration mission was performed by carrying on a series of fully three-dimensional Navier-Stokes computations. The static aerodynamic performance was then discussed, including the surface pressure, lift and drag coefficient, lift-to-drag ratio with the numerical and engineering method. Computation results shown that the shock layer is thin because of lower effective specific heat ratio, and that calculated results from both methods agree well with each other, and is consistent with the reference data. Aerodynamic performance analysis shows that CG location determines trim characteristics and pitch stability, and certain radially and axially shift of the CG location can alter the capsule lifting entry performance, which is of vital significance for the aerodynamic configuration des0ign and inner instrument layout of the Mars entry capsule.

Keywords: Mars entry capsule, static aerodynamics, computational fluid dynamics, hypersonic

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Authors: Gómez R. Marta, Martín M. Jesús María

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The general aim of this paper identifies the areas of highest concentration of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) around the incinerator through the use of dispersion models. Atmospheric dispersion models are useful tools for estimating and prevent the impact of emissions from a particular source in air quality. These models allow considering different factors that influence in air pollution: source characteristics, the topography of the receiving environment and weather conditions to predict the pollutants concentration. The PCDD/Fs, after its emission into the atmosphere, are deposited on water or land, near or far from emission source depending on the size of the associated particles and climatology. In this way, they are transferred and mobilized through environmental compartments. The modelling of PCDD/Fs was carried out with following tools: Atmospheric Dispersion Model Software (ADMS) and Surfer. ADMS is a dispersion model Gaussian plume, used to model the impact of air quality industrial facilities. And Surfer is a program of surfaces which is used to represent the dispersion of pollutants on a map. For the modelling of emissions, ADMS software requires the following input parameters: characterization of emission sources (source type, height, diameter, the temperature of the release, flow rate, etc.) meteorological and topographical data (coordinate system), mainly. The study area was set at 5 Km around the incinerator and the first population center nearest to focus PCDD/Fs emission is about 2.5 Km, approximately. Data were collected during one year (2013) both PCDD/Fs emissions of the incinerator as meteorology in the study area. The study has been carried out during period's average that legislation establishes, that is to say, the output parameters are taking into account the current legislation. Once all data required by software ADMS, described previously, are entered, and in order to make the representation of the spatial distribution of PCDD/Fs concentration and the areas affecting them, the modelling was proceeded. In general, the dispersion plume is in the direction of the predominant winds (Southwest and Northeast). Total levels of PCDD/Fs usually found in air samples, are from <2 pg/m3 for remote rural areas, from 2-15 pg/m3 in urban areas and from 15-200 pg/m3 for areas near to important sources, as can be an incinerator. The results of dispersion maps show that maximum concentrations are the order of 10-8 ng/m3, well below the values considered for areas close to an incinerator, as in this case.

Keywords: atmospheric dispersion, dioxin, furan, incinerator

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Authors: Numair A. Siddiqui, Usman Muhammad, Manoj J. Mathew, Ramkumar M., Benjamin Sautter, Muhammad A. K. El-Ghali, David Menier, Shiqi Zhang

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The sedimentological parameterization of shallow-marine siliciclastic reservoirs in terms of reservoir quality and heterogeneity from outcrop study can help improve the subsurface reservoir prediction. An architectural analysis has documented variations in sandstone geometry and rock properties within shallow-marine sandstone exposed in the Miocene Sandakan Formation of Sabah, Borneo. This study demonstrates reservoir sandstone quality assessment for subsurface rock evaluation, from well-exposed successions of the Sandakan Formation, Borneo, with which applicable analogues can be identified. The analyses were based on traditional conventional field investigation of outcrops, grain-size and petrographic studies of hand specimens of different sandstone facies and gamma-ray and permeability measurements. On the bases of these evaluations, the studied sandstone was grouped into three qualitative reservoir rock classes; high (Ø=18.10 – 43.60%; k=1265.20 – 5986.25 mD), moderate (Ø=17.60 – 37%; k=21.36 – 568 mD) and low quality (Ø=3.4 – 15.7%; k=3.21 – 201.30 mD) for visualization and prediction of subsurface reservoir quality. These results provided analogy for shallow marine sandstone reservoir complexity that can be utilized in the evaluation of reservoir quality of regional and subsurface analogues.

Keywords: architecture and sedimentology, subsurface rock evaluation, reservoir quality, borneo

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Authors: M. Baldini, H. K. Jacobsen, M. Jaccard

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In light of the international efforts to comply with the Paris agreement and emission targets for future energy systems, Denmark and Canada are among the front-runner countries dealing with climate change. The experiences in the energy sector have seen both countries coping with trade-offs between investments in renewable energy technologies and energy efficiency, thus tackling the climate issue from the supply and demand side respectively. On the demand side, the industrial sector is going through a remarkable transformation, with implementation of energy efficiency measures, change of input fuel for end-use processes and forecasted electrification as main features under the spotlight. By looking at Canada and Denmark's experiences as pathfinders on the demand and supply approach to climate change, it is possible to obtain valuable experience that may be applied to other countries aiming at the same goal. This paper presents a comparative study on industrial energy efficiency between Canada and Denmark. The study focuses on technologies and system options, policy design and implementation and modelling methodologies when implementing industrial energy savings in optimization models in comparison to simulation models. The study identifies gaps and junctures in the approach towards climate change actions and, learning from each other, lessen the differences to further foster the adoption of energy efficiency measurements in the industrial sector, aiming at reducing energy consumption and, consequently, CO₂ emissions.

Keywords: industrial energy efficiency, comparative study, CO₂ reduction, energy system modelling

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Authors: Varun Dongre, Stefan Pirker, Stefan Heinrich

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Over the last decades, the numerical modelling of fluidized bed processes has become feasible even for industrial processes. Commonly, continuous two-fluid models are applied to describe large-scale fluidization. In order to allow for coarse grids novel two-fluid models account for unresolved sub-grid heterogeneities. However, computational efforts remain high – in the order of several hours of compute-time for a few seconds of real-time – thus preventing the representation of long-term phenomena such as heating or particle conversion processes. In order to overcome this limitation, data-based recurrence computational fluid dynamics (rCFD) has been put forward in recent years. rCFD can be regarded as a data-based method that relies on the numerical predictions of a conventional short-term simulation. This data is stored in a database and then used by rCFD to efficiently time-extrapolate the flow behavior in high spatial resolution. This study will compare the numerical predictions of rCFD simulations with those of corresponding full CFD reference simulations for lab-scale and pilot-scale fluidized beds. In assessing the predictive capabilities of rCFD simulations, we focus on solid mixing and secondary gas holdup. We observed that predictions made by rCFD simulations are highly sensitive to numerical parameters such as diffusivity associated with face swaps. We achieved a computational speed-up of four orders of magnitude (10,000 time faster than classical TFM simulation) eventually allowing for real-time simulations of fluidized beds. In the next step, we apply the checkerboarding technique by introducing gas tracers subjected to convection and diffusion. We then analyze the concentration profiles by observing mixing, transport of gas tracers, insights about the convective and diffusive pattern of the gas tracers, and further towards heat and mass transfer methods. Finally, we run rCFD simulations and calibrate them with numerical and physical parameters compared with convectional Two-fluid model (full CFD) simulation. As a result, this study gives a clear indication of the applicability, predictive capabilities, and existing limitations of rCFD in the realm of fluidization modelling.

Keywords: multiphase flow, recurrence CFD, two-fluid model, industrial processes

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Authors: Arsalan Ghaderi

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Introduction: Inactivity is one of the most important risk factors for cardiovascular disease. According to the study prevalence of inactivity in Iran, about 67.5% and in the staff, and especially nurses, are similar. The inflammatory index (HS-CRP) is highly predictive of the progression of these diseases. Physical activity education is very important in preventing these diseases. One of the modern educational methods is web-based theory-based education. Methods: This is a semi-experimental interventional study which was conducted in Isfahan and Kurdistan universities of medical sciences in two stages. A cross-sectional study was done to determine the status of physical activity and its predictive factors. Then, intervention was performed, and six months later the data were retrieved. The data was collected using a demographic questionnaire, an integrative model of behavior prediction constructs, a standard physical activity questionnaire and (HS-CRP) test. Data were analyzed by SPSS software. Results: Physical activity was low in 66.6% of nurses, 25.4% were moderate and 8% severe. According to Pearson correlation matrix, the highest correlation was found between behavioral intention and skill structures (0.553**), subjective norms (0.222**) and self-efficacy (0.198**). The relationship between age and physical activity in the first study was reverse and significant. After intervention, there was a significant change in attitudes, self-efficacy, skill and behavioral intention in the intervention group. This change was significant in attitudes, self-efficacy and environmental conditions of the control group. HS-CRP index decreased significantly after intervention in both groups, but there was not a significant relationship between inflammatory index and physical activity score. The change in physical activity level was significant only in the control group. Conclusion: Despite the effect of educational intervention on attitude, self-efficacy, skill, and behavioral intention, the results showed that if factors such as environmental factors are not corrected, training and changing structures cannot lead to physical activity behavior. On the other hand, no correlation between physical activity and HS-CRP showed that this index can be influenced by other factors, and this should be considered in any intervention to reduce the HS-CRP index.

Keywords: HS-CRP, integrative model of behavior prediction, physical activity, nurses, web-based education

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Authors: Amira Abdelhafez, Rufus Brunt

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Geothermal energy in Egypt represents a significant untapped renewable resource that can reduce reliance on conventional power generation. Exploiting these geothermal resources depends on depth, temperature range, and geological characteristics. The intracontinental rift setting of the Gulf of Suez (GoS)-Red Sea rift is a favourable tectonic setting for convection-dominated geothermal plays. The geothermal gradient across the GoS ranges from 24.9 to 86.66 °C/km, with a heat flow of 31-127.2 mW/m². Surface expressions of convective heat loss emerge along the gulf flanks as hot springs (e.g., Hammam Faraun) accompanying deeper geothermal resources. These thermal anomalies are driven mainly by the local tectonic configuration. Characterizing the structural framework of major faults and their control on reservoir properties and subsurface hydrothermal fluid circulation is vital for geothermal applications in the gulf. The geothermal play systems of the GoS depend on structural and lithological properties that contribute to heat storage and vertical transport. Potential geothermal reservoirs include the Nubia sandstones, which, due to their thickness, continuity, and contact with hot basement rocks at a mean depth of 3 km, create an extensive reservoir for geothermal fluids. To develop these geothermal resources for energy production, defining the permeability anisotropy of the reservoir due to faults and facies variation is a crucial step in our study, particularly the evaluation of influence on thermal breakthrough and production rates.

Keywords: geothermal, October field, site specific study, reservoir modelling

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Authors: L. Al Ahmad, C. Latrille, D. Hainos, D. Blanc, M. Clausse

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A two-dimensional finite volume axisymmetric model is developed to predict the simultaneous heat and mass transfers during the drying of industrial sludge. The simulations were run using COMSOL-Multiphysics 3.5a. The input parameters of the numerical model were acquired from a preliminary experimental work. Results permit to establish correlations describing the evolution of the various parameters as a function of the drying temperature and the sludge water content. The selection and coupling of the equation are validated based on the drying kinetics acquired experimentally at a temperature range of 45-65 °C and absolute pressure range of 200-1000 mbar. The model, incorporating the heat and mass transfer mechanisms at different operating conditions, shows simulated values of temperature and water content. Simulated results are found concordant with the experimental values, only at the first and last drying stages where sludge shrinkage is insignificant. Simulated and experimental results show that sludge drying is favored at high temperatures and low pressure. As experimentally observed, the drying time is reduced by 68% for drying at 65 °C compared to 45 °C under 1 atm. At 65 °C, a 200-mbar absolute pressure vacuum leads to an additional reduction in drying time estimated by 61%. However, the drying rate is underestimated in the intermediate stage. This rate underestimation could be improved in the model by considering the shrinkage phenomena that occurs during sludge drying.

Keywords: industrial sludge drying, heat transfer, mass transfer, mathematical modelling

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Authors: C. Castro-Egler, A. Durán-Escalante, A. García-González

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This paper presents a method to generate a finite element model of the human auditory inner ear system. The geometric model has been realized using 2D images from a virtual model of temporal bones. A point cloud has been gotten manually from those images to construct a whole mesh with hexahedral elements. The main difference with the predecessor models is the spiral shape of the cochlea with its three scales completely defined: scala tympani, scala media and scala vestibuli; which are separate by basilar membrane and Reissner membrane. To validate this model, numerical simulations have been realised with two models: an isolated inner ear and a whole model of human auditory system. Ideal conditions of displacement are applied over the oval window in the isolated Inner Ear model. The whole model is made up of the outer auditory channel, the tympani, the ossicular chain, and the inner ear. The boundary condition for the whole model is 1Pa over the auditory channel entrance. The numerical simulations by FEM have been done using a harmonic analysis with a frequency range between 100-10.000 Hz with an interval of 100Hz. The following results have been carried out: basilar membrane displacement; the scala media pressure according to the cochlea length and the transfer function of the middle ear normalized with the pressure in the tympanic membrane. The basilar membrane displacements and the pressure in the scala media make it possible to validate the response in frequency of the basilar membrane.

Keywords: finite elements method, human auditory system model, numerical analysis, 3D modelling cochlea

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Authors: Jun Wang, Ge Zhang

Abstract:

Machine learning has been used in many areas today. It thrives at reviewing large volumes of data and identifying patterns and trends that might not be apparent to a human. Given the huge potential benefit and the amount of data available in the financial market, it is not surprising to see machine learning applied to various financial products. While future prices of financial securities are extremely difficult to forecast, we study them from a different angle. Instead of trying to forecast future prices, we apply machine learning algorithms to predict the direction of future price movement, in particular, whether a sector Exchange Traded Fund (ETF) would outperform or underperform the market in the next week or in the next month. We apply several machine learning algorithms for this prediction. The algorithms are Linear Discriminant Analysis (LDA), k-Nearest Neighbors (KNN), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Neural Networks (NN). We show that these machine learning algorithms, most notably GNB and NN, have some predictive power in forecasting out-performance and under-performance out of sample. We also try to explore whether it is possible to utilize the predictions from these algorithms to outperform the buy-and-hold strategy of the S&P 500 index. The trading strategy to explore out-performance predictions does not perform very well, but the trading strategy to explore under-performance predictions can earn higher returns than simply holding the S&P 500 index out of sample.

Keywords: machine learning, ETF prediction, dynamic trading, asset allocation

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Authors: Adil Balla Elkrail

Abstract:

Visual MODFLOW computer codes were selected to simulate head distribution, calculate the groundwater budgets of the area, and evaluate the effect of external stresses on the groundwater head and to demonstrate how the groundwater model can be used as a comparative technique in order to optimize utilization of the groundwater resource. A conceptual model of the study area, aquifer parameters, boundary, and initial conditions were used to simulate the flow model. The trial-and-error technique was used to calibrate the model. The most important criteria used to check the calibrated model were Root Mean Square error (RMS), Mean Absolute error (AM), Normalized Root Mean Square error (NRMS) and mass balance. The maps of the simulated heads elaborated acceptable model calibration compared to observed heads map. A time length of eight years and the observed heads of the year 2004 were used for model prediction. The predictive simulation showed that the continuation of pumping will cause relatively high changes in head distribution and components of groundwater budget whereas, the low deficit computed (7122 m3/d) between inflows and outflows cannot create a significant drawdown of the potentiometric level. Hence, the area under consideration may represent a high permeability and productive zone and strongly recommended for further groundwater development.

Keywords: aquifers, model simulation, groundwater, calibrations, trail-and- error, prediction

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Authors: Francisco Reyes, Hector Ramirez

Abstract:

In the coastal zones are home to mangrove swamps, coral reefs, and seagrass ecosystems, which are the most biodiverse and fragile on the planet. These areas support a great diversity of marine life; they are also extraordinarily important for humans in the provision of food, water, wood, and other associated goods and services; they also contribute to climate regulation. The lack of an automated model that generates information on the dynamics of changes in coastlines and coastal erosion is identified as a central problem. Coastlines were determined from 1984 to 2020 on the Google Earth platform Engine from Landsat and Sentinel images, using the Normalized Differential Water Index (MNDWI) and Digital Shoreline Analysis System (DSAS) v5.0. Starting from the 2020 coastline, the 10-year prediction (Year 2031) was determined with the erosion of 238.32 hectares and an accretion of 181.96 hectares, while the 20-year prediction (Year 2041) will be presented an erosion of 544.04 hectares and an accretion of 133.94 hectares. The erosion and accretion of Playa Muelle in the municipality of Puerto Colombia were established, which will register the highest value of erosion. The coverage that presented the greatest change was that of artificialized Territories.

Keywords: coastline, coastal erosion, MNDWI, Google Earth Engine, Colombia

Procedia PDF Downloads 124

Authors: Mathieu Le Cam, Tejaswinee Darure, Mateusz Pawlucki

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Climate control of cabin aircraft is traditionally conditioned as a single unit by the environmental control system. Cabin temperature is controlled by the crew while passengers of the aircraft have control on the gaspers providing fresh air from the above head area. The small nozzles are difficult to reach and adjust to meet the passenger’s needs in terms of flow and direction. More dedicated control over the near environment of each passenger can be beneficial in many situations. The European project COCOON, funded under Clean Sky 2, aims at developing and demonstrating a microclimate conditioning module (MCM) integrated into a standard economy 3-seat row. The system developed will lead to improved passenger comfort with more control on their personal thermal area. This study focuses on the assessment of thermal comfort of passengers in the cabin aircraft through simulation on the TAITherm modelling platform. A first analysis investigates thermal comfort and sensation of passengers in varying cabin environmental conditions: from cold to very hot scenarios, with and without MCM installed in the seats. The modelling platform is also used to evaluate the impact of different physiologies of passengers on their thermal comfort as well as different seat locations. Under the current cabin conditions, a passenger of a 50th percentile body size is feeling uncomfortably cool due to the high velocity cabin air ventilation. The simulation shows that the in-seat MCM developed in COCOON project improves the thermal comfort of the passenger.

Keywords: cabin aircraft, in-seat HVAC, microclimate conditioning module, thermal comfort

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Authors: Baladitya Swaika, Rahul Khatry

Abstract:

Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

Procedia PDF Downloads 176

Authors: Maxime A. J. Kuitche, Ruxandra M. Botez, Arthur Guillemin

Abstract:

As the Unmanned Aerial Systems have found diverse utilities in both military and civil aviation, the necessity to obtain an accurate aerodynamic model has shown an enormous growth of interest. Recent modeling techniques are procedures using optimization algorithms and statistics that require many flight tests and are therefore extremely demanding in terms of costs. This paper presents a procedure to estimate the aerodynamic behavior of an unmanned aerial system from a numerical approach using computational fluid dynamic analysis. The study was performed using an unstructured mesh obtained from a grid convergence analysis at a Mach number of 0.14, and at an angle of attack of 0°. The flow around the aircraft was described using a standard k-ω turbulence model. Thus, the Reynold Averaged Navier-Stokes (RANS) equations were solved using ANSYS FLUENT software. The method was applied on the UAS-S45 designed and manufactured by Hydra Technologies in Mexico. The lift, the drag, and the pitching moment coefficients were obtained at different angles of attack for several flight conditions defined in terms of altitudes and Mach numbers. The results obtained from the Computational Fluid Dynamics analysis were compared with the results obtained by using the DATCOM semi-empirical procedure. This comparison has indicated that our approach is highly accurate and that the aerodynamic model obtained could be useful to estimate the flight dynamics of the UAS-S45.

Keywords: aerodynamic modelling, CFD Analysis, ANSYS FLUENT, UAS-S45

Procedia PDF Downloads 376