Search results for: numerical weather prediction

Authors: Alia Alghosoun, Michael Herty, Mohammed Seaid

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We present a new class of numerical techniques to solve shallow water flows over dry areas including run-up. Many recent investigations on wave run-up in coastal areas are based on the well-known shallow water equations. Numerical simulations have also performed to understand the effects of several factors on tsunami wave impact and run-up in the presence of coastal areas. In all these simulations the shallow water equations are solved in entire domain including dry areas and special treatments are used for numerical solution of singularities at these dry regions. In the present study we propose a new method to deal with these difficulties by reformulating the shallow water equations into a new system to be solved only in the wetted domain. The system is obtained by a change in the coordinates leading to a set of equations in a moving domain for which the wet/dry interface is the reconstructed using the wave speed. To solve the new system we present a finite volume method of Lax-Friedrich type along with a modified method of characteristics. The method is well-balanced and accurately resolves dam-break problems over dry areas.

Keywords: dam-break problems, finite volume method, run-up waves, shallow water flows, wet/dry interfaces

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Authors: Hanifeh Imanian, Morteza Kolahdoozan

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The purpose of this study is to present a mathematical phrase for calculating the dispersion rate of spilled oil in water column under non-breaking waves. In this regard, a multiphase numerical model is applied for which waves and oil phase were computed concurrently, and accuracy of its hydraulic calculations have been proven. More than 200 various scenarios of oil spilling in wave waters were simulated using the multiphase numerical model and its outcome were collected in a database. The recorded results were investigated to identify the major parameters affected vertical oil dispersion and finally 6 parameters were identified as main independent factors. Furthermore, some statistical tests were conducted to identify any relationship between the dependent variable (dispersed oil mass in the water column) and independent variables (water wave specifications containing height, length and wave period and spilled oil characteristics including density, viscosity and spilled oil mass). Finally, a mathematical-statistical relationship is proposed to predict dispersed oil in marine waters. To verify the proposed relationship, a laboratory example available in the literature was selected. Oil mass rate penetrated in water body computed by statistical regression was in accordance with experimental data was predicted. On this occasion, it was necessary to verify the proposed mathematical phrase. In a selected laboratory case available in the literature, mass oil rate penetrated in water body computed by suggested regression. Results showed good agreement with experimental data. The validated mathematical-statistical phrase is a useful tool for oil dispersion prediction in oil spill events in marine areas.

Keywords: dispersion, marine environment, mathematical-statistical relationship, oil spill

Procedia PDF Downloads 235

Authors: Orkun Yavuz, Ferhat Kadioğlu, M. Emin Ercan

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This study aims to investigate the effects of repeated high loadings on the performance of adhesively-bonded Single Lap Joints (SLJs) by employing both experimental and numerical approaches. A projectile with a mass of 1.25 gr and density of 11.3 gr/cm3 was fired at the joints with a velocity of about 280 m/s using a specially designed experimental set-up, and the impact was recorded via a high-speed camera. The SLJs were manufactured from 6061 aluminum adherend (AA6061) material and an adhesive film. The joints, which have an adherend thickness of 4 mm and overlap length of 15 mm, were subjected to up to 3 shots for the ballistic test, followed by quasi-static tensile testing. The impacted joints, then, were compared to the non-impacted and one-shot impacted ones, which was a subject of investigation carried out before. It was found that while the joints subjected to 2 shots mechanically deteriorated, those subjected to 3 shots experienced a complete failure at the end of the experiment. A numerical study was also conducted using an ABAQUS package program. While the adherends were modelled using the Johnson-Cook deformation parameters, an elastoplastic behavior of the adhesive was used as input data in the analyses. It seems the experimental results confirm the numerical ones.

Keywords: ballistic tests, adhesive joints, numerical analysis, SLJ

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Authors: Ejoh Nonso Francis

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: Irrigation scheduling is a critical aspect of sustainable agriculture as it ensures optimal use of water resources, reduces water waste, and enhances crop yields. This paper presents a case study of a farm in Onitsha, Anambra State, Nigeria, where irrigation scheduling was optimized using a combination of soil moisture sensors and weather data. The study aimed to evaluate the effectiveness of this approach in improving water use efficiency and crop productivity. The results showed that the optimized irrigation scheduling approach led to a 30% reduction in water use while increasing crop yield by 20%. The study demonstrates the potential of technology-based irrigation scheduling to enhance sustainable agriculture in Nigeria and beyond.

Keywords: irrigation scheduling, sustainable agriculture, soil moisture sensors, weather data, water use efficiency, crop productivity, nigeria, onitsha, anambra state, technology-based irrigation scheduling, water resources, environmental degradation, crop water requirements, overwatering, water waste, farming systems, scalability

Procedia PDF Downloads 79

Authors: Nana Adjoah Mbroh, Suares Clovis Oukouomi Noutchie

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Singularly perturbed problems are parameter dependent problems, and they play major roles in the modelling of real-life situational problems in applied sciences. Thus, designing efficient numerical schemes to solve these problems is of much interest since the exact solutions of such problems may not even exist. Generally, singularly perturbed problems are identified by a small parameter multiplying at least the highest derivative in the equation. The presence of this parameter causes the solution of these problems to be characterized by rapid oscillations. This unique feature renders classical numerical schemes inefficient since they are unable to capture the behaviour of the exact solution in the part of the domain where the rapid oscillations are present. In this paper, a numerical scheme is proposed to solve a singularly perturbed Volterra Integro-differential equation. The scheme is based on the midpoint rule and employs the non-standard finite difference scheme to solve the differential part whilst the composite trapezoidal rule is used for the integral part. A fully fledged error estimate is performed, and Richardson extrapolation is applied to accelerate the convergence of the scheme. Numerical simulations are conducted to confirm the theoretical findings before and after extrapolation.

Keywords: midpoint rule, non-standard finite difference schemes, Richardson extrapolation, singularly perturbed problems, trapezoidal rule, uniform convergence

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Authors: Marzieh Karimihaghighi, Carlos Castillo

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This work studies how Machine Learning (ML) may be used to increase the effectiveness of a criminal recidivism risk assessment tool, RisCanvi. The two key dimensions of this analysis are predictive accuracy and algorithmic fairness. ML-based prediction models obtained in this study are more accurate at predicting criminal recidivism than the manually-created formula used in RisCanvi, achieving an AUC of 0.76 and 0.73 in predicting violent and general recidivism respectively. However, the improvements are small, and it is noticed that algorithmic discrimination can easily be introduced between groups such as national vs foreigner, or young vs old. It is described how effectiveness and algorithmic fairness objectives can be balanced, applying a method in which a single error disparity in terms of generalized false positive rate is minimized, while calibration is maintained across groups. Obtained results show that this bias mitigation procedure can substantially reduce generalized false positive rate disparities across multiple groups. Based on these results, it is proposed that ML-based criminal recidivism risk prediction should not be introduced without applying algorithmic bias mitigation procedures.

Keywords: algorithmic fairness, criminal risk assessment, equalized odds, recidivism

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Authors: Florin Frunzulica, Alexandru Dumitrache, Octavian Preotu

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The Coanda effect consists of the tendency of a jet to remain attached to a sufficiently long/large convex surface. Flows deflected by a curved surface have caused great interest during last fifty years a major interest in the study of this phenomenon is caused by the possibility of using this effect to aircraft with short take-off and landing, for thrust vectoring. It is also used in applications involving mixing two of more fluids, noise attenuation, ventilation, etc. The paper proposes the numerical study of an aerodynamic configuration that can passively amplify the Coanda effect. On a wing flaps with predetermined configuration, a channel is applied between two particular zones, a low-pressure one and a high-pressure another one, respectively. The secondary flow through this channel yields a gap between the jet and the convex surface, maintaining the jet attached on a longer distance. The section altering-based active control of the secondary flow through the channel controls the attachment of the jet to the surface and automatically controls the deviation angle of the jet. The numerical simulations have been performed in Ansys Fluent for a series of wing flaps-channel configurations with varying jet velocity. The numerical results are in good agreement with experimental results.

Keywords: blowing jet, CFD, Coanda effect, circulation control

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Authors: Etienne Gaborit, Dorothy Durnford, Daniel Deacu, Marco Carrera, Nathalie Gauthier, Camille Garnaud, Vincent Fortin

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A new experimental streamflow forecasting system was recently implemented at the Environment and Climate Change Canada’s (ECCC) Canadian Centre for Meteorological and Environmental Prediction (CCMEP). It relies on CaLDAS (Canadian Land Data Assimilation System) for the assimilation of surface variables, and on a surface prediction system that feeds a routing component. The surface energy and water budgets are simulated with the SVS (Soil, Vegetation, and Snow) Land-Surface Scheme (LSS) at 2.5-km grid spacing over Canada. The routing component is based on the Watroute routing scheme at 1-km grid spacing for the Great Lakes and Nelson River watersheds. The system is run in two distinct phases: an analysis part and a forecast part. During the analysis part, CaLDAS outputs are used to force the routing system, which performs streamflow assimilation. In forecast mode, the surface component is forced with the Canadian GEM atmospheric forecasts and is initialized with a CaLDAS analysis. Streamflow performances of this new system are presented over 2019. Performances are compared to the current ECCC’s operational streamflow forecasting system, which is different from the new experimental system in many aspects. These new streamflow forecasts are also compared to persistence. Overall, the new streamflow forecasting system presents promising results, highlighting the need for an elaborated assimilation phase before performing the forecasts. However, the system is still experimental and is continuously being improved. Some major recent improvements are presented here and include, for example, the assimilation of snow cover data from remote sensing, a backward propagation of assimilated flow observations, a new numerical scheme for the routing component, and a new reservoir model.

Keywords: assimilation system, distributed physical model, offline hydro-meteorological chain, short-term streamflow forecasts

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Authors: Yusuf Yilmaz, Gamze Gediz Ilis

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Gas Nitrogen where has a boiling point of -189.52oC at atmospheric pressure widely used in the industry. Nitrogen that used in the industry should be transported in liquid form to the plant area. Ambient air vaporizer (AAV) generally used for vaporization of cryogenic gases such as liquid nitrogen (LN2), liquid oxygen (LOX), liquid natural gas (LNG), and liquid argon (LAR) etc. AAV is a group of star-shaped fin vaporizer. The design and the effect of the shape of fins of the vaporizer is one of the most important criteria for the performance of the vaporizer. In this study, the performance of AAV working with liquid nitrogen was analyzed numerically in a star-shaped aluminum finned pipe. The numerical analysis is performed in order to investigate the heat capacity of the vaporizer per meter pipe length. By this way, the vaporizer capacity can be predicted for the industrial applications. In order to achieve the validation of the numerical solution, the experimental setup is constructed. The setup includes a liquid nitrogen tank with a pressure of 9 bar. The star-shaped aluminum finned tube vaporizer is connected to the LN2 tank. The inlet and the outlet pressure and temperatures of the LN2 of the vaporizer are measured. The mass flow rate of the LN2 is also measured and collected. The comparison of the numerical solution is performed by these measured data. The ambient conditions of the experiment are given as boundary conditions to the numerical model. The surface tension and contact angle have a significant effect on the boiling of liquid nitrogen. Average heat transfer coefficient including convective and nucleated boiling components should be obtained for liquid nitrogen saturated flow boiling in the finned tube. Fluent CFD module is used to simulate the numerical solution. The turbulent k-ε model is taken to simulate the liquid nitrogen flow. The phase change is simulated by using the evaporation-condensation approach used with user-defined functions (UDF). The comparison of the numerical and experimental results will be shared in this study. Besides, the performance capacity of the star-shaped finned pipe vaporizer will be calculated in this study. Based on this numerical analysis, the performance of the vaporizer per unit length can be predicted for the industrial applications and the suitable pipe length of the vaporizer can be found for the special cases.

Keywords: liquid nitrogen, numerical modeling, two-phase flow, cryogenics

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Authors: Mohamed Elhakeem, A. N. Thanos Papanicolaou, Christopher Wilson, Yi-Jia Chang

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In this study, a physically-based, modelling framework was developed to predict saturated hydraulic conductivity (KSAT) dynamics in the Clear Creek Watershed (CCW), Iowa. The modelling framework integrated selected pedotransfer functions and watershed models with geospatial tools. A number of pedotransfer functions and agricultural watershed models were examined to select the appropriate models that represent the study site conditions. Models selection was based on statistical measures of the models’ errors compared to the KSAT field measurements conducted in the CCW under different soil, climate and land use conditions. The study has shown that the predictions of the combined pedotransfer function of Rosetta and the Water Erosion Prediction Project (WEPP) provided the best agreement to the measured KSAT values in the CCW compared to the other tested models. Therefore, Rosetta and WEPP were integrated with the Geographic Information System (GIS) tools for visualization of the data in forms of geospatial maps and prediction of KSAT variability in CCW due to the seasonal changes in climate and land use activities.

Keywords: saturated hydraulic conductivity, pedotransfer functions, watershed models, geospatial tools

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Authors: Tomas Berhanu Bekele

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Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea of avoiding traffic instabilities and homogenizing traffic flow in such a way that the risk of accidents is minimized and traffic flow is maximized. Lately, Intelligent Transport Systems (ITS) has become an important area of research to solve such road traffic-related issues for making smart decisions. It links people, roads and vehicles together using communication technologies to increase safety and mobility. Moreover, accurate prediction of road traffic is important to manage traffic congestion. The aim of this study is to develop an ANN model for the prediction of traffic flow and to compare the ANN model with the linear regression model of traffic flow predictions. Data extraction was carried out in intervals of 15 minutes from the video player. Video of mixed traffic flow was taken and then counted during office work in order to determine the traffic volume. Vehicles were classified into six categories, namely Car, Motorcycle, Minibus, mid-bus, Bus, and Truck vehicles. The average time taken by each vehicle type to travel the trap length was measured by time displayed on a video screen.

Keywords: intelligent transport system (ITS), traffic flow prediction, artificial neural network (ANN), linear regression

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Authors: Andrey Lebedev, Ilya Dmitrenok, JooYoung Lee, Leonard Johard

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Top-k shortest path routing problem is an extension of finding the shortest path in a given network. Shortest path is one of the most essential measures as it reveals the relations between two nodes in a network. However, in many real world networks, whose diameters are small, top-k shortest path is more interesting as it contains more information about the network topology. Many variations to compute top-k shortest paths have been studied. In this paper, we apply an efficient top-k shortest distance routing algorithm to the link prediction problem and test its efficacy. We compare the results with other base line and state-of-the-art methods as well as with the shortest path. Then, we also propose a top-k distance based graph matching algorithm.

Keywords: graph matching, link prediction, shortest path, similarity

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Authors: Hyun-Kyu Cho, Jun-Su Kim, Choon-Geun Huh, Geon Lee Young-Chul Park

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In this study, the relationship between flow properties and fluid projection distance look into connection for shape variations of foam monitor. A numerical analysis technique for fluid analysis of a foam monitor was developed for the prediction. Shape of foam monitor the flow path of fluid flow according to the shape, The fluid losses were calculated from flow analysis result.. The modified model used the length increase model of the flow path, and straight line of the model. Inlet pressure was 7 [bar] and external was atmosphere codition. am. The results showed that the length increase model of the flow path and straight line of the model was improved in the nozzle projection distance.

Keywords: injection performance, finite element method, foam monitor, Projection distance

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Authors: Yinwei Lin, Cha'o-Kuang Chen

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In this article, the Laplace Adomian transform method (LADM) and double decomposition method (DDM) are used to solve the annular hyperbolic profile fins with variable thermal conductivity. As the thermal conductivity parameter ε is relatively large, the numerical solution using DDM become incorrect. Moreover, when the terms of DDM are more than seven, the numerical solution using DDM is very complicated. However, the present method can be easily calculated as terms are over seven and has more precisely numerical solutions. As the thermal conductivity parameter ε is relatively large, LADM also has better accuracy than DDM.

Keywords: fins, thermal conductivity, Laplace transform, Adomian, nonlinear

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Authors: Ahmed Ramadhan Al-Obaidi, Jassim Alhamid

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In this investigation, the flow pattern, characteristics of thermal-hydraulic, and improvement of heat transfer performance are evaluated using a numerical technique in three dimensions corrugated pipe heat exchanger. The modification was made under different corrugated pipe geometrical parameters, including corrugated ring angle (CRA), distance between corrugated ring (DBCR), and corrugated diameter (CD), the range of Re number from 2000 to 12000. The numerical results are validated with available experimental data. The numerical outcomes reveal that there is an important change in flow field behaviour and a significant increase in friction factor and improvement in heat transfer performance owing to the use of the corrugated shape in the heat exchanger pipe as compared to the conventional smooth pipe. Using corrugated pipe with different configurations makes the flow more turbulence, flow separation, boundary layer distribution, flow mixing, and that leads to augmenting the performance of heat transfer. Moreover, the value of pressure drop, and the Nusselt number increases as the corrugated pipe geometrical parameters increase. Furthermore, the corrugation configuration shapes have an important influence on the thermal evaluation performance factor, and the maximum value was more than 1.3. Numerical simulation can be performed to predict the various geometrical configurations effects on fluid flow, thermal performance, and heat transfer enhancement.

Keywords: corrugated ring angle, corrugated diameter, Nusselt number, heat transfer

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Authors: Faizul Azly Abd Dzubir, Muhammad F. Othman

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Conventional structural health monitoring approach for mechanical equipment uses inspection data from Non-Destructive Testing (NDT) during plant shut down window and fitness for service evaluation to estimate the integrity of the equipment that is prone to crack damage. Yet, this forecast is fraught with uncertainty because it is often based on assumptions of future operational parameters, and the prediction is not continuous or online. Advanced Diagnostic and Prognostic Technology (ADaPT) uses Acoustic Emission (AE) technology and a stochastic prognostic model to provide real-time monitoring and prediction of mechanical defects or cracks. The forecast can help the plant authority handle their cracked equipment before it ruptures, causing an unscheduled shutdown of the facility. The ADaPT employs process historical data trending, finite element analysis, fitness for service, and probabilistic statistical analysis to develop a prediction model for crack initiation and growth due to mechanical damage. The prediction model is combined with live equipment operating data for real-time prediction of the remaining life span owing to fracture. ADaPT was devised at a hot combined feed exchanger (HCFE) that had suffered creep crack damage. The ADaPT tool predicts the initiation of a crack at the top weldment area by April 2019. During the shutdown window in April 2019, a crack was discovered and repaired. Furthermore, ADaPT successfully advised the plant owner to run at full capacity and improve output by up to 7% by April 2019. ADaPT was also used on a coke drum that had extensive fatigue cracking. The initial cracks are declared safe with ADaPT, with remaining crack lifetimes extended another five (5) months, just in time for another planned facility downtime to execute repair. The prediction model, when combined with plant information data, allows plant operators to continuously monitor crack propagation caused by mechanical damage for improved maintenance planning and to avoid costly shutdowns to repair immediately.

Keywords: mechanical damage, cracks, continuous monitoring tool, remaining life, acoustic emission, prognostic model

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Authors: Prashant Verma

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Commercial banks are facing immense pressure, including financial disintermediation, interest rate volatility and digital ways of finance. Retaining an existing customer is 5 to 25 less expensive than acquiring a new one. This paper explores customer churn prediction, based on various statistical & machine learning models and uses under-sampling, to improve the predictive power of these models. The results show that out of the various machine learning models, Random Forest which predicts the churn with 78% accuracy, has been found to be the most powerful model for the scenario. Customer vintage, customer’s age, average balance, occupation code, population code, average withdrawal amount, and an average number of transactions were found to be the variables with high predictive power for the churn prediction model. The model can be deployed by the commercial banks in order to avoid the customer churn so that they may retain the funds, which are kept by savings bank (SB) customers. The article suggests a customized campaign to be initiated by commercial banks to avoid SB customer churn. Hence, by giving better customer satisfaction and experience, the commercial banks can limit the customer churn and maintain their deposits.

Keywords: savings bank, customer churn, customer retention, random forests, machine learning, under-sampling

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Authors: Pitchayanin Sukholthaman

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Gradually accumulated, the enormous amount of waste has caused tremendous adverse impacts to the world. Bangkok, Thailand, is chosen as an urban city of a developing country having coped with serious MSW problems due to the vast amount of waste generated, ineffective and improper waste management problems. Waste generation is the most important factor for successful planning of MSW management system. Thus, the prediction of MSW is a very important role to understand MSW distribution and characteristic; to be used for strategic planning issues. This study aims to find influencing variables that affect the amount of Bangkok MSW generation quantity.

Keywords: MSW generation, MSW quantity prediction, MSW management, multiple regression, Bangkok

Procedia PDF Downloads 423

Authors: Kudzanayi Chiteka, Wellington Makondo

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The whole world is on the drive to go green owing to the negative effects of burning fossil fuels. Therefore, there is immediate need to identify and utilise alternative renewable energy sources. Among these energy sources solar energy is one of the most dominant in Zimbabwe. Solar power plants used to generate electricity are entirely dependent on solar radiation. For planning purposes, solar radiation values should be known in advance to make necessary arrangements to minimise the negative effects of the absence of solar radiation due to cloud cover and other naturally occurring phenomena. This research focused on the prediction of Global Horizontal Irradiation values for the sixth day given values for the past five days. Artificial intelligence techniques were used in this research. Three models were developed based on Support Vector Machines, Radial Basis Function, and Feed Forward Back-Propagation Artificial neural network. Results revealed that Support Vector Machines gives the best results compared to the other two with a mean absolute percentage error (MAPE) of 2%, Mean Absolute Error (MAE) of 0.05kWh/m²/day root mean square (RMS) error of 0.15kWh/m²/day and a coefficient of determination of 0.990. The other predictive models had prediction accuracies of MAPEs of 4.5% and 6% respectively for Radial Basis Function and Feed Forward Back-propagation Artificial neural network. These two models also had coefficients of determination of 0.975 and 0.970 respectively. It was found that prediction of GHI values for the future days is possible using artificial intelligence-based predictive models.

Keywords: solar energy, global horizontal irradiation, artificial intelligence, predictive models

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Authors: Abderazak Guettaf

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The mathematical models of the physical phenomena interacting in inductive plasma were described by the physics equations of the continuous mediums. A 3D model based on magnetic potential vector and electric scalar potential (A, V) formulation is used. The finished volume method is applied to electromagnetic equation, to obtain the field distribution inside the plasma. The numerical results of the method developed on a basic model designed starting from a real three-dimensional model were exposed. From the mathematical model 3D spreading assumptions and boundary conditions, we evaluated the electric field in the load and we have developed a numerical code made under the MATLAB environment, all verifying the effectiveness and validity of this code.

Keywords: electric field, 3D magnetic potential vector and electric scalar potential (A, V) formulation, finished volumes, annular plasma

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Authors: Aymen Laadhari

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We present in this paper a fully implicit finite element method tailored for the numerical modeling of inextensible fluidic membranes in a surrounding Newtonian fluid. We consider a highly simplified version of the Canham-Helfrich model for phospholipid membranes, in which the bending force and spontaneous curvature are disregarded. The coupled problem is formulated in a fully Eulerian framework and the membrane motion is tracked using the level set method. The resulting nonlinear problem is solved by a Newton-Raphson strategy, featuring a quadratic convergence behavior. A monolithic solver is implemented, and we report several numerical experiments aimed at model validation and illustrating the accuracy of the proposed method. We show that stability is maintained for significantly larger time steps with respect to an explicit decoupling method.

Keywords: finite element method, level set, Newton, membrane

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Authors: Narjes Ebrahimi, Soheila Alyasin, Navid Nezafat, Hossein Esmailzadeh, Younes Ghasemi, Seyed Hesamodin Nabavizadeh

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Immunotherapy with allergy vaccines is of great importance in allergen-specific immunotherapy. In recent years, B-cell epitope-based vaccines have attracted considerable attention and the prediction of epitopes is crucial to design these types of allergy vaccines. B-cell epitopes might be linear or conformational. The prerequisite for the identification of conformational epitopes is the information about allergens' tertiary structures. Bioinformatics approaches have paved the way towards the design of epitope-based allergy vaccines through the prediction of tertiary structures and epitopes. Mite allergens are one of the major allergy contributors. Several mite allergens can elicit allergic reactions; however, their structures and epitopes are not well established. So, B-cell epitopes of various groups of mite allergens (24 allergens in 6 allergen groups) were predicted in the present work. Tertiary structures of 17 allergens with unknown structure were predicted and refined with RaptorX and GalaxyRefine servers, respectively. The predicted structures were further evaluated by Rampage, ProSA-web, ERRAT and Verify 3D servers. Linear and conformational B-cell epitopes were identified with Ellipro, Bcepred, and DiscoTope 2 servers. To improve the accuracy level, consensus epitopes were selected. Fifty-four conformational and 133 linear consensus epitopes were predicted. Furthermore, overlapping epitopes in each allergen group were defined, following the sequence alignment of the allergens in each group. The predicted epitopes were also compared with the experimentally identified epitopes. The presented results provide valuable information for further studies about allergy vaccine design.

Keywords: B-cell epitope, Immunotherapy, In silico prediction, Mite allergens, Tertiary structure

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Authors: Nieves M. C. Talavera-Prieto, Abel G. M. Ferreira, António T. G. Portugal, Rui J. Moreira, Jaime B. Santos

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The knowledge of biodiesel density over large ranges of temperature and pressure is important for predicting the behavior of fuel injection and combustion systems in diesel engines, and for the optimization of such systems. In this study, cottonseed oil was transesterified into biodiesel and its density was measured at temperatures between 288 K and 358 K and pressures between 0.1 MPa and 30 MPa, with expanded uncertainty estimated as ±1.6 kg.m^-3. Experimental pressure-volume-temperature (pVT) cottonseed data was used along with literature data relative to other 18 biodiesels, in order to build a database used to test the correlation of density with temperarure and pressure using the Goharshadi–Morsali–Abbaspour equation of state (GMA EoS). To our knowledge, this is the first that density measurements are presented for cottonseed biodiesel under such high pressures, and the GMA EoS used to model biodiesel density. The new tested EoS allowed correlations within 0.2 kg•m-3 corresponding to average relative deviations within 0.02%. The built database was used to develop and test a new full predictive model derived from the observed linear relation between density and degree of unsaturation (DU), which depended from biodiesel FAMEs profile. The average density deviation of this method was only about 3 kg.m-3 within the temperature and pressure limits of application. These results represent appreciable improvements in the context of density prediction at high pressure when compared with other equations of state.

Keywords: biodiesel density, correlation, equation of state, prediction

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Authors: A. Brahma

Abstract:

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: Wei Tian, Jie Liang, Hammad Naveed

Abstract:

Beta-barrel membrane proteins are found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. They carry out diverse biological functions, including pore formation, membrane anchoring, enzyme activity, and bacterial virulence. In addition, beta-barrel membrane proteins increasingly serve as scaffolds for bacterial surface display and nanopore-based DNA sequencing. Due to difficulties in experimental structure determination, they are sparsely represented in the protein structure databank and computational methods can help to understand their biophysical principles. We have developed a novel computational method to predict the 3D structure of beta-barrel membrane proteins using evolutionary coupling (EC) constraints and a reduced state space. Combined with an empirical potential function, we can successfully predict strand register at > 80% accuracy for a set of 49 non-homologous proteins with known structures. This is a significant improvement from previous results using EC alone (44%) and using empirical potential function alone (73%). Our method is general and can be applied to genome-wide structural prediction.

Keywords: beta-barrel membrane proteins, structure prediction, evolutionary constraints, reduced state space

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Authors: Tauha Hussain Ali, Shabir Hussain Khahro, Nafees Ahmed Memon

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Accurate prediction of project time for planning and bid preparation stage should contain realistic dates. Constructors use their experience to estimate the project duration for the new projects, which is based on intuitions. It has been a constant concern to both researchers and constructors to analyze the accurate prediction of project duration for bid preparation stage. In Pakistan, such study for time cost relationship has been lacked to predict duration performance for the construction projects. This study is an attempt to explore the time cost relationship that would conclude with a mathematical model to predict the time for the drainage rehabilitation projects in the province of Sindh, Pakistan. The data has been collected from National Engineering Services (NESPAK), Pakistan and regression analysis has been carried out for the analysis of results. Significant relationship has been found between time and cost of the construction projects in Sindh and the generated mathematical model can be used by the constructors to predict the project duration for the upcoming projects of same nature. This study also provides the professionals with a requisite knowledge to make decisions regarding project duration, which is significantly important to win the projects at the bid stage.

Keywords: BTC Model, project time, relationship of time cost, regression

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Authors: J. Skibinski, P. Caban, T. Wejrzanowski, K. J. Kurzydlowski

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In the present study numerical simulations of epitaxial growth of gallium nitride in Metal Organic Vapor Phase Epitaxy reactor AIX-200/4RF-S are addressed. The aim of this study was to design the optimal fluid flow and thermal conditions for obtaining the most homogeneous product. Since there are many agents influencing reactions on the crystal growth area such as temperature, pressure, gas flow or reactor geometry, it is difficult to design optimal process. Variations of process pressure and hydrogen mass flow rates have been considered. According to the fact that it’s impossible to determine experimentally the exact distribution of heat and mass transfer inside the reactor during crystal growth, detailed 3D modeling has been used to get an insight of the process conditions. Numerical simulations allow to understand the epitaxial process by calculation of heat and mass transfer distribution during growth of gallium nitride. Including chemical reactions in the numerical model allows to calculate the growth rate of the substrate. The present approach has been applied to enhance the performance of AIX-200/4RF-S reactor.

Keywords: computational fluid dynamics, finite volume method, epitaxial growth, gallium nitride

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Authors: Karel Frana, Sylvio Simon

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The objective of the paper is a numerical study of heat transfer between perforated metal plates and the surrounding air flows. Different perforation structures can nowadays be found in various industrial products. Besides improving the mechanical properties, the perforations can intensify the heat transfer as well. The heat transfer coefficient depends on a wide range of parameters such as type of perforation, size, shape, flow properties of the surrounding air etc. The paper was focused on three different perforation structures which have been investigated from the point of the view of the production in the previous studies. To determine the heat coefficients and the Nusselt numbers, the numerical simulation approach was adopted. The calculations were performed using the OpenFOAM software. The three-dimensional, unstable, turbulent and incompressible air flow around the perforated surface metal plate was considered.

Keywords: perforations, convective heat transfers, turbulent flows, numerical simulations

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Authors: K. Djellabi, M. E. H. Latreche

Abstract:

The induction heating phenomenon depends on various factors, making the problem highly nonlinear. The mathematical analysis of this problem in most cases is very difficult and it is reduced to simple cases. Another knowledge of induction heating systems is generated in production environments, but these trial-error procedures are long and expensive. The numerical models of induction heating problem are another approach to reduce abovementioned drawbacks. This paper deals with the simulation model of induction heating problem. The simulation model of induction heating system in COMSOL Multiphysics is created. In this work we present results of numerical simulations of induction heating process in pieces of cylindrical shapes, in an inductor with four coils. The modeling of the inducting heating process was made with the software COMSOL Multiphysics Version 4.2a, for the study we present the temperature charts.

Keywords: induction heating, electromagnetic field, inductor, numerical simulation, finite element

Procedia PDF Downloads 317

Authors: E. Akpinar, A. Erol, M.F. Cakir

Abstract:

Precast reinforced concrete (RC) structures are excellent alternatives for construction world all over the globe, thanks to their rapid erection phase, ease mounting process, better quality and reasonable prices. Such structures are rather popular for industrial buildings. For the sake of economic importance of such industrial buildings as well as significance of safety, like every other type of structures, performance assessment and structural risk analysis are important. Fragility curves are powerful tools for damage projection and assessment for any sort of building as well as precast structures. In this study, a comparative review of current knowledge on fragility analysis of industrial precast RC structures were presented and findings in previous studies were compiled. Effects of different structural variables, parameters and building geometries as well as soil conditions on fragility analysis of precast structures are reviewed. It was aimed to briefly present the information in the literature about the procedure of damage probability prediction including fragility curves for such industrial facilities. It is found that determination of the aforementioned structural parameters as well as selecting analysis procedure are critically important for damage prediction of industrial precast RC structures using fragility curves.

Keywords: damage prediction, fragility curve, industrial buildings, precast reinforced concrete structures

Procedia PDF Downloads 191