Search results for: prediction modelling
3476 Early Design Prediction of Submersible Maneuvers
Authors: Hernani Brinati, Mardel de Conti, Moyses Szajnbok, Valentina Domiciano
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This study brings a mathematical model and examples for the numerical prediction of submersible maneuvers in the horizontal and in the vertical planes. The geometry of the submarine is here taken as a body of revolution plus a sail, two horizontal and two vertical rudders. The model includes the representation of the hull resistance and of the propeller thrust and torque, what enables to consider the variation of the longitudinal component of the velocity of the ship when maneuvering. The hydrodynamic forces are represented through power series expansions of the acceleration and velocity components. The hydrodynamic derivatives for the body of revolution are mostly estimated based on fundamental principles applicable to the flow around airplane fuselages in the subsonic regime. The hydrodynamic forces for the sail and rudders are estimated based on a finite aspect ratio wing theory. The objective of this study is to build an expedite model for submarine maneuvers prediction, based on fundamental principles, which may be convenient in the early stages of the ship design. This model is tested against available numerical and experimental data.Keywords: submarine maneuvers, submarine, maneuvering, dynamics
Procedia PDF Downloads 6263475 Consumer Behaviour Model for Apparel E-Tailers Using Structural Equation Modelling
Authors: Halima Akhtar, Abhijeet Chandra
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The paper attempts to analyze the factors that influence the Consumer Behavior to purchase apparel through the internet. The intentions to buy apparels online were based on in terms of user style, orientation, size and reputation of the merchant, social influence, perceived information utility, perceived ease of use, perceived pleasure and attractiveness and perceived trust and risk. The basic framework used was Technology acceptance model to explain apparels acceptance. A survey was conducted to gather the data from 200 people. The measures and hypotheses were analyzed using Correlation testing and would be further validated by the Structural Equation Modelling. The implications of the findings for theory and practice could be used by marketers of online apparel websites. Based on the values obtained, we can conclude that the factors such as social influence, Perceived information utility, attractiveness and trust influence the decision for a user to buy apparels online. The major factors which are found to influence an online apparel buying decision are ease of use, attractiveness that a website can offer and the trust factor which a user shares with the website.Keywords: E-tailers, consumer behaviour, technology acceptance model, structural modelling
Procedia PDF Downloads 1723474 An Advanced Numerical Tool for the Design of Through-Thickness Reinforced Composites for Electrical Applications
Authors: Bing Zhang, Jingyi Zhang, Mudan Chen
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Fibre-reinforced polymer (FRP) composites have been extensively utilised in various industries due to their high specific strength, e.g., aerospace, renewable energy, automotive, and marine. However, they have relatively low electrical conductivity than metals, especially in the out-of-plane direction. Conductive metal strips or meshes are typically employed to protect composites when designing lightweight structures that may be subjected to lightning strikes, such as composite wings. Unfortunately, this approach downplays the lightweight advantages of FRP composites, thereby limiting their potential applications. Extensive studies have been undertaken to improve the electrical conductivity of FRP composites. The authors are amongst the pioneers who use through-thickness reinforcement (TTR) to tailor the electrical conductivity of composites. Compared to the conventional approaches using conductive fillers, the through-thickness reinforcement approach has been proven to be able to offer a much larger improvement to the through-thickness conductivity of composites. In this study, an advanced high-fidelity numerical modelling strategy is presented to investigate the effects of through-thickness reinforcement on both the in-plane and out-of-plane electrical conductivities of FRP composites. The critical micro-structural features of through-thickness reinforced composites incorporated in the modelling framework are 1) the fibre waviness formed due to TTR insertion; 2) the resin-rich pockets formed due to resin flow in the curing process following TTR insertion; 3) the fibre crimp, i.e., fibre distortion in the thickness direction of composites caused by TTR insertion forces. In addition, each interlaminar interface is described separately. An IMA/M21 composite laminate with a quasi-isotropic stacking sequence is employed to calibrate and verify the modelling framework. The modelling results agree well with experimental measurements for bothering in-plane and out-plane conductivities. It has been found that the presence of conductive TTR can increase the out-of-plane conductivity by around one order, but there is less improvement in the in-plane conductivity, even at the TTR areal density of 0.1%. This numerical tool provides valuable references as a design tool for through-thickness reinforced composites when exploring their electrical applications. Parametric studies are undertaken using the numerical tool to investigate critical parameters that affect the electrical conductivities of composites, including TTR material, TTR areal density, stacking sequence, and interlaminar conductivity. Suggestions regarding the design of electrical through-thickness reinforced composites are derived from the numerical modelling campaign.Keywords: composite structures, design, electrical conductivity, numerical modelling, through-thickness reinforcement
Procedia PDF Downloads 793473 Building Information Modelling Implementation in the Lifecycle of Sustainable Buildings
Authors: Scarlet Alejandra Romano, Joni Kareco
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The three pillars of sustainability (social, economic and environmental) are relevant concepts to the Architecture, Engineering, and Construction (AEC) industry because of the increase of international agreements and guidelines related to this topic during the last years. Considering these three pillars, the AEC industry faces important challenges, for instance, to decrease the carbon emissions (environmental challenge), design sustainable spaces for people (social challenge), and improve the technology of this field to reduce costs and environmental problems (economic and environmental challenge). One alternative to overcome these challenges is Building Information Modelling program (BIM) because according to several authors, this technology improves the performance of the sustainable buildings in all their lifecycle phases. The main objective of this paper is to explore and analyse the current advantages and disadvantages of the BIM implementation in the life-cycle of sustainable buildings considering the three pillars of sustainability as analysis parameters. The methodology established to achieve this objective is exploratory-descriptive with the literature review technique. The partial results illustrate that despite the BIM disadvantages and the lack of information about its social sustainability advantages, this software represents a significant opportunity to improve the three sustainable pillars of the sustainable buildings.Keywords: building information modelling, building lifecycle analysis, sustainability, sustainable buildings
Procedia PDF Downloads 1733472 Blood Glucose Measurement and Analysis: Methodology
Authors: I. M. Abd Rahim, H. Abdul Rahim, R. Ghazali
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There is numerous non-invasive blood glucose measurement technique developed by researchers, and near infrared (NIR) is the potential technique nowadays. However, there are some disagreements on the optimal wavelength range that is suitable to be used as the reference of the glucose substance in the blood. This paper focuses on the experimental data collection technique and also the analysis method used to analyze the data gained from the experiment. The selection of suitable linear and non-linear model structure is essential in prediction system, as the system developed need to be conceivably accurate.Keywords: linear, near-infrared (NIR), non-invasive, non-linear, prediction system
Procedia PDF Downloads 4513471 Application and Assessment of Artificial Neural Networks for Biodiesel Iodine Value Prediction
Authors: Raquel M. De sousa, Sofiane Labidi, Allan Kardec D. Barros, Alex O. Barradas Filho, Aldalea L. B. Marques
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Several parameters are established in order to measure biodiesel quality. One of them is the iodine value, which is an important parameter that measures the total unsaturation within a mixture of fatty acids. Limitation of unsaturated fatty acids is necessary since warming of a higher quantity of these ones ends in either formation of deposits inside the motor or damage of lubricant. Determination of iodine value by official procedure tends to be very laborious, with high costs and toxicity of the reagents, this study uses an artificial neural network (ANN) in order to predict the iodine value property as an alternative to these problems. The methodology of development of networks used 13 esters of fatty acids in the input with convergence algorithms of backpropagation type were optimized in order to get an architecture of prediction of iodine value. This study allowed us to demonstrate the neural networks’ ability to learn the correlation between biodiesel quality properties, in this case iodine value, and the molecular structures that make it up. The model developed in the study reached a correlation coefficient (R) of 0.99 for both network validation and network simulation, with Levenberg-Maquardt algorithm.Keywords: artificial neural networks, biodiesel, iodine value, prediction
Procedia PDF Downloads 5973470 Prediction of the Mechanical Power in Wind Turbine Powered Car Using Velocity Analysis
Authors: Abdelrahman Alghazali, Youssef Kassem, Hüseyin Çamur, Ozan Erenay
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Savonius is a drag type vertical axis wind turbine. Savonius wind turbines have a low cut-in speed and can operate at low wind speed. This makes it suitable for electricity or mechanical generation in low-power applications such as individual domestic installations. Therefore, the primary purpose of this work was to investigate the relationship between the type of Savonius rotor and the torque and mechanical power generated. And it was to illustrate how the type of rotor might play an important role in the prediction of mechanical power of wind turbine powered car. The main purpose of this paper is to predict and investigate the aerodynamic effects by means of velocity analysis on the performance of a wind turbine powered car by converting the wind energy into mechanical energy to overcome load that rotates the main shaft. The predicted results based on theoretical analysis were compared with experimental results obtained from literature. The percentage of error between the two was approximately around 20%. Prediction of the torque was done at a wind speed of 4 m/s, and an angular velocity of 130 RPM according to meteorological statistics in Northern Cyprus.Keywords: mechanical power, torque, Savonius rotor, wind car
Procedia PDF Downloads 3223469 Numerical Method for Productivity Prediction of Water-Producing Gas Well with Complex 3D Fractures: Case Study of Xujiahe Gas Well in Sichuan Basin
Authors: Hong Li, Haiyang Yu, Shiqing Cheng, Nai Cao, Zhiliang Shi
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Unconventional resources have gradually become the main direction for oil and gas exploration and development. However, the productivity of gas wells, the level of water production, and the seepage law in tight fractured gas reservoirs are very different. These are the reasons why production prediction is so difficult. Firstly, a three-dimensional multi-scale fracture and multiphase mathematical model based on an embedded discrete fracture model (EDFM) is established. And the material balance method is used to calculate the water body multiple according to the production performance characteristics of water-producing gas well. This will help construct a 'virtual water body'. Based on these, this paper presents a numerical simulation process that can adapt to different production modes of gas wells. The research results show that fractures have a double-sided effect. The positive side is that it can increase the initial production capacity, but the negative side is that it can connect to the water body, which will lead to the gas production drop and the water production rise both rapidly, showing a 'scissor-like' characteristic. It is worth noting that fractures with different angles have different abilities to connect with the water body. The higher the angle of gas well development, the earlier the water maybe break through. When the reservoir is a single layer, there may be a stable production period without water before the fractures connect with the water body. Once connected, a 'scissors shape' will appear. If the reservoir has multiple layers, the gas and water will produce at the same time. The above gas-water relationship can be matched with the gas well production date of the Xujiahe gas reservoir in the Sichuan Basin. This method is used to predict the productivity of a well with hydraulic fractures in this gas reservoir, and the prediction results are in agreement with on-site production data by more than 90%. It shows that this research idea has great potential in the productivity prediction of water-producing gas wells. Early prediction results are of great significance to guide the design of development plans.Keywords: EDFM, multiphase, multilayer, water body
Procedia PDF Downloads 1893468 Biohydrogen and Potential Vinegar Production from Agricultural Wastes Using Thermotoga neopolitana
Authors: Nidhi Nalin
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This study is theoretical modelling of the fermentation process of glucose in agricultural wastes like discarded peaches to produce hydrogen, acetic acid, and carbon dioxide using Thermotoga neopolitana bacteria. The hydrogen gas produced in this process can be used in hydrogen fuel cells to generate power, and the fermented broth with acetic acid and salts could be utilized as salty vinegar if enough acetic acid is produced. The theoretical modelling was done using SuperPro software, and the results indicated how much sugar (discarded peaches) is required to produce both hydrogen and vinegar for the process to be profitable.Keywords: fermentation, thermotoga, hydrogen, vinegar, biofuel
Procedia PDF Downloads 1493467 Groundwater Level Prediction Using hybrid Particle Swarm Optimization-Long-Short Term Memory Model and Performance Evaluation
Authors: Sneha Thakur, Sanjeev Karmakar
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This paper proposed hybrid Particle Swarm Optimization (PSO) – Long-Short Term Memory (LSTM) model for groundwater level prediction. The evaluation of the performance is realized using the parameters: root mean square error (RMSE) and mean absolute error (MAE). Ground water level forecasting will be very effective for planning water harvesting. Proper calculation of water level forecasting can overcome the problem of drought and flood to some extent. The objective of this work is to develop a ground water level forecasting model using deep learning technique integrated with optimization technique PSO by applying 29 years data of Chhattisgarh state, In-dia. It is important to find the precise forecasting in case of ground water level so that various water resource planning and water harvesting can be managed effectively.Keywords: long short-term memory, particle swarm optimization, prediction, deep learning, groundwater level
Procedia PDF Downloads 673466 Computer Simulation Studies of Aircraft Wing Architectures on Vibration Responses
Authors: Shengyong Zhang, Mike Mikulich
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Vibration is a crucial limiting consideration in the analysis and design of airplane wing structures to avoid disastrous failures due to the propagation of existing cracks in the material. In this paper, we build CAD models of aircraft wings to capture the design intent with configurations. Subsequent FEA vibration analysis is performed to study the natural vibration properties and impulsive responses of the resulting user-defined wing models. This study reveals the variations of the wing’s vibration characteristics with respect to changes in its structural configurations. Integrating CAD modelling and FEA vibration analysis enables designers to improve wing architectures for implementing design requirements in the preliminary design stage.Keywords: aircraft wing, CAD modelling, FEA, vibration analysis
Procedia PDF Downloads 1533465 Equivalent Circuit Modelling of Active Reflectarray Antenna
Authors: M. Y. Ismail, M. Inam
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This paper presents equivalent circuit modeling of active planar reflectors which can be used for the detailed analysis and characterization of reflector performance in terms of lumped components. Equivalent circuit representation has been proposed for PIN diodes and liquid crystal based active planar reflectors designed within X-band frequency range. A very close agreement has been demonstrated between equivalent circuit results, 3D EM simulated results as well as measured scattering parameter results. In the case of measured results, a maximum discrepancy of 1.05dB was observed in the reflection loss performance, which can be attributed to the losses occurred during measurement process.Keywords: Equivalent circuit modelling, planar reflectors, reflectarray antenna, PIN diode, liquid crystal
Procedia PDF Downloads 2803464 Building Information Modelling-Based Diminished Reality Visualisation to Facilitate Building Renovation Projects
Authors: Roghieh Eskandari, Ali Motamedi
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There is a significant demand for renovation as-built assets are aging. To plan for a desirable and comfortable indoor environment, stakeholders use simulation technics to assess potential renovation scenarios with the innovative designs. Diminished Reality (DR), which is a technique of visually removing unwanted objects from the real-world scene in real-time, can contribute to the renovation design visualization for stakeholders by removing existing structures and assets from the scene. Using DR, the objects to be demolished or changed will be visually removed from the scene for a better understanding of the intended design scenarios for stakeholders. This research proposes an integrated system for renovation plan visualization using Building Information Modelling (BIM) data and mixed reality (MR) technologies. It presents a BIM-based DR method that utilizes a textured BIM model of the environment to accurately register the virtual model of the occluded background to the physical world in real-time. This system can facilitate the simulation of the renovation plan by visually diminishing building elements in an indoor environment.Keywords: diminished reality, building information modelling, mixed reality, stock renovation
Procedia PDF Downloads 1083463 Inversely Designed Chipless Radio Frequency Identification (RFID) Tags Using Deep Learning
Authors: Madhawa Basnayaka, Jouni Paltakari
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Fully passive backscattering chipless RFID tags are an emerging wireless technology with low cost, higher reading distance, and fast automatic identification without human interference, unlike already available technologies like optical barcodes. The design optimization of chipless RFID tags is crucial as it requires replacing integrated chips found in conventional RFID tags with printed geometric designs. These designs enable data encoding and decoding through backscattered electromagnetic (EM) signatures. The applications of chipless RFID tags have been limited due to the constraints of data encoding capacity and the ability to design accurate yet efficient configurations. The traditional approach to accomplishing design parameters for a desired EM response involves iterative adjustment of design parameters and simulating until the desired EM spectrum is achieved. However, traditional numerical simulation methods encounter limitations in optimizing design parameters efficiently due to the speed and resource consumption. In this work, a deep learning neural network (DNN) is utilized to establish a correlation between the EM spectrum and the dimensional parameters of nested centric rings, specifically square and octagonal. The proposed bi-directional DNN has two simultaneously running neural networks, namely spectrum prediction and design parameters prediction. First, spectrum prediction DNN was trained to minimize mean square error (MSE). After the training process was completed, the spectrum prediction DNN was able to accurately predict the EM spectrum according to the input design parameters within a few seconds. Then, the trained spectrum prediction DNN was connected to the design parameters prediction DNN and trained two networks simultaneously. For the first time in chipless tag design, design parameters were predicted accurately after training bi-directional DNN for a desired EM spectrum. The model was evaluated using a randomly generated spectrum and the tag was manufactured using the predicted geometrical parameters. The manufactured tags were successfully tested in the laboratory. The amount of iterative computer simulations has been significantly decreased by this approach. Therefore, highly efficient but ultrafast bi-directional DNN models allow rapid and complicated chipless RFID tag designs.Keywords: artificial intelligence, chipless RFID, deep learning, machine learning
Procedia PDF Downloads 333462 Effect of Drying on the Concrete Structures
Authors: A. Brahma
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The drying of hydraulics materials is unavoidable and conducted to important spontaneous deformations. In this study, we show that it is possible to describe the drying shrinkage of the high-performance concrete by a simple expression. A multiple regression model was developed for the prediction of the drying shrinkage of the high-performance concrete. The assessment of the proposed model has been done by a set of statistical tests. The model developed takes in consideration the main parameters of confection and conservation. There was a very good agreement between drying shrinkage predicted by the multiple regression model and experimental results. The developed model adjusts easily to all hydraulic concrete types.Keywords: hydraulic concretes, drying, shrinkage, prediction, modeling
Procedia PDF Downloads 3563461 Capability Prediction of Machining Processes Based on Uncertainty Analysis
Authors: Hamed Afrasiab, Saeed Khodaygan
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Prediction of machining process capability in the design stage plays a key role to reach the precision design and manufacturing of mechanical products. Inaccuracies in machining process lead to errors in position and orientation of machined features on the part, and strongly affect the process capability in the final quality of the product. In this paper, an efficient systematic approach is given to investigate the machining errors to predict the manufacturing errors of the parts and capability prediction of corresponding machining processes. A mathematical formulation of fixture locators modeling is presented to establish the relationship between the part errors and the related sources. Based on this method, the final machining errors of the part can be accurately estimated by relating them to the combined dimensional and geometric tolerances of the workpiece – fixture system. This method is developed for uncertainty analysis based on the Worst Case and statistical approaches. The application of the presented method is illustrated through presenting an example and the computational results are compared with the Monte Carlo simulation results.Keywords: process capability, machining error, dimensional and geometrical tolerances, uncertainty analysis
Procedia PDF Downloads 3013460 Analysis of Active Compounds in Thai Herbs by near Infrared Spectroscopy
Authors: Chaluntorn Vichasilp, Sutee Wangtueai
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This study aims to develop a new method to detect active compounds in Thai herbs (1-deoxynojirimycin (DNJ) in mulberry leave, anthocyanin in Mao and curcumin in turmeric) using near infrared spectroscopy (NIRs). NIRs is non-destructive technique that rapid, non-chemical involved and low-cost determination. By NIRs and chemometrics technique, it was found that the DNJ prediction equation conducted with partial least square regression with cross-validation had low accuracy R2 (0.42) and SEP (31.87 mg/100g). On the other hand, the anthocyanin prediction equation showed moderate good results (R2 and SEP of 0.78 and 0.51 mg/g) with Multiplication scattering correction at wavelength of 2000-2200 nm. The high absorption could be observed at wavelength of 2047 nm and this model could be used as screening level. For curcumin prediction, the good result was obtained when applied original spectra with smoothing technique. The wavelength of 1400-2500 nm was created regression model with R2 (0.68) and SEP (0.17 mg/g). This model had high NIRs absorption at a wavelength of 1476, 1665, 1986 and 2395 nm, respectively. NIRs showed prospective technique for detection of some active compounds in Thai herbs.Keywords: anthocyanin, curcumin, 1-deoxynojirimycin (DNJ), near infrared spectroscopy (NIRs)
Procedia PDF Downloads 3693459 Urban Runoff Modeling of Ungauged Volcanic Catchment in Madinah, Western Saudi Arabia
Authors: Fahad Alahmadi, Norhan Abd Rahman, Mohammad Abdulrazzak, Zulikifli Yusop
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Runoff prediction of ungauged catchment is still a challenging task especially in arid regions with a unique land cover such as volcanic basalt rocks where geological weathering and fractures are highly significant. In this study, Bathan catchment in Madinah western Saudi Arabia was selected for analysis. The aim of this paper is to evaluate different rainfall loss methods; soil conservation Services curve number (SCS-CN), green-ampt and initial-constant rate. Different direct runoff methods were evaluated: soil conservation services dimensionless unit hydrograph (SCS-UH), Snyder unit hydrograph and Clark unit hydrograph. The study showed the superiority of SCS-CN loss method and Clark unit hydrograph method for ungauged catchment where there is no observed runoff data.Keywords: urban runoff modelling, arid regions, ungauged catchments, volcanic rocks, Madinah, Saudi Arabia
Procedia PDF Downloads 3923458 A Polynomial Relationship for Prediction of COD Removal Efficiency of Cyanide-Inhibited Wastewater in Aerobic Systems
Authors: Eze R. Onukwugha
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The presence of cyanide in wastewater is known to inhibit the normal functioning of bio-reactors since it has the tendency to poison reactor micro-organisms. Bench scale models of activated sludge reactors with varying aspect ratios were operated for the treatment of cassava wastewater at several values of hydraulic retention time (HRT). The different values of HRT were achieved by the use of a peristaltic pump to vary the rate of introduction of the wastewater into the reactor. The main parameters monitored are the cyanide concentration and respective COD values of the influent and effluent. These observed values were then transformed into a mathematical model for the prediction of treatment efficiency.Keywords: wastewater, aspect ratio, cyanide-inhibited wastewater, modeling
Procedia PDF Downloads 683457 Modelling of Solidification in a Latent Thermal Energy Storage with a Finned Tube Bundle Heat Exchanger Unit
Authors: Remo Waser, Simon Maranda, Anastasia Stamatiou, Ludger J. Fischer, Joerg Worlitschek
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In latent heat storage, a phase change material (PCM) is used to store thermal energy. The heat transfer rate during solidification is limited and considered as a key challenge in the development of latent heat storages. Thus, finned heat exchangers (HEX) are often utilized to increase the heat transfer rate of the storage system. In this study, a new modeling approach to calculating the heat transfer rate in latent thermal energy storages with complex HEX geometries is presented. This model allows for an optimization of the HEX design in terms of costs and thermal performance of the system. Modeling solidification processes requires the calculation of time-dependent heat conduction with moving boundaries. Commonly used computational fluid dynamic (CFD) methods enable the analysis of the heat transfer in complex HEX geometries. If applied to the entire storage, the drawback of this approach is the high computational effort due to small time steps and fine computational grids required for accurate solutions. An alternative to describe the process of solidification is the so-called temperature-based approach. In order to minimize the computational effort, a quasi-stationary assumption can be applied. This approach provides highly accurate predictions for tube heat exchangers. However, it shows unsatisfactory results for more complex geometries such as finned tube heat exchangers. The presented simulation model uses a temporal and spatial discretization of heat exchanger tube. The spatial discretization is based on the smallest possible symmetric segment of the HEX. The heat flow in each segment is calculated using finite volume method. Since the heat transfer fluid temperature can be derived using energy conservation equations, the boundary conditions at the inner tube wall is dynamically updated for each time step and segment. The model allows a prediction of the thermal performance of latent thermal energy storage systems using complex HEX geometries with considerably low computational effort.Keywords: modelling of solidification, finned tube heat exchanger, latent thermal energy storage
Procedia PDF Downloads 2613456 Software Reliability Prediction Model Analysis
Authors: Lela Mirtskhulava, Mariam Khunjgurua, Nino Lomineishvili, Koba Bakuria
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Software reliability prediction gives a great opportunity to measure the software failure rate at any point throughout system test. A software reliability prediction model provides with the technique for improving reliability. Software reliability is very important factor for estimating overall system reliability, which depends on the individual component reliabilities. It differs from hardware reliability in that it reflects the design perfection. Main reason of software reliability problems is high complexity of software. Various approaches can be used to improve the reliability of software. We focus on software reliability model in this article, assuming that there is a time redundancy, the value of which (the number of repeated transmission of basic blocks) can be an optimization parameter. We consider given mathematical model in the assumption that in the system may occur not only irreversible failures, but also a failure that can be taken as self-repairing failures that significantly affect the reliability and accuracy of information transfer. Main task of the given paper is to find a time distribution function (DF) of instructions sequence transmission, which consists of random number of basic blocks. We consider the system software unreliable; the time between adjacent failures has exponential distribution.Keywords: exponential distribution, conditional mean time to failure, distribution function, mathematical model, software reliability
Procedia PDF Downloads 4543455 Simulation on Influence of Environmental Conditions on Part Distortion in Fused Deposition Modelling
Authors: Anto Antony Samy, Atefeh Golbang, Edward Archer, Alistair McIlhagger
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Fused deposition modelling (FDM) is one of the additive manufacturing techniques that has become highly attractive in the industrial and academic sectors. However, parts fabricated through FDM are highly susceptible to geometrical defects such as warpage, shrinkage, and delamination that can severely affect their function. Among the thermoplastic polymer feedstock for FDM, semi-crystalline polymers are highly prone to part distortion due to polymer crystallization. In this study, the influence of FDM processing conditions such as chamber temperature and print bed temperature on the induced thermal residual stress and resulting warpage are investigated using the 3D transient thermal model for a semi-crystalline polymer. The thermo-mechanical properties and the viscoelasticity of the polymer, as well as the crystallization physics, which considers the crystallinity of the polymer, are coupled with the evolving temperature gradient of the print model. From the results, it was observed that increasing the chamber temperature from 25°C to 75°C lead to a decrease of 1.5% residual stress, while decreasing bed temperature from 100°C to 60°C, resulted in a 33% increase in residual stress and a significant rise of 138% in warpage. The simulated warpage data is validated by comparing it with the measured warpage values of the samples using 3D scanning.Keywords: finite element analysis, fused deposition modelling, residual stress, warpage
Procedia PDF Downloads 1753454 Modelling Fluoride Pollution of Groundwater Using Artificial Neural Network in the Western Parts of Jharkhand
Authors: Neeta Kumari, Gopal Pathak
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Artificial neural network has been proved to be an efficient tool for non-parametric modeling of data in various applications where output is non-linearly associated with input. It is a preferred tool for many predictive data mining applications because of its power , flexibility, and ease of use. A standard feed forward networks (FFN) is used to predict the groundwater fluoride content. The ANN model is trained using back propagated algorithm, Tansig and Logsig activation function having varying number of neurons. The models are evaluated on the basis of statistical performance criteria like Root Mean Squarred Error (RMSE) and Regression coefficient (R2), bias (mean error), Coefficient of variation (CV), Nash-Sutcliffe efficiency (NSE), and the index of agreement (IOA). The results of the study indicate that Artificial neural network (ANN) can be used for groundwater fluoride prediction in the limited data situation in the hard rock region like western parts of Jharkhand with sufficiently good accuracy.Keywords: Artificial neural network (ANN), FFN (Feed-forward network), backpropagation algorithm, Levenberg-Marquardt algorithm, groundwater fluoride contamination
Procedia PDF Downloads 5373453 A Unified Fitting Method for the Set of Unified Constitutive Equations for Modelling Microstructure Evolution in Hot Deformation
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Constitutive equations are very important in finite element (FE) modeling, and the accuracy of the material constants in the equations have significant effects on the accuracy of the FE models. A wide range of constitutive equations are available; however, fitting the material constants in the constitutive equations could be complex and time-consuming due to the strong non-linearity and relationship between the constants. This work will focus on the development of a set of unified MATLAB programs for fitting the material constants in the constitutive equations efficiently. Users will only need to supply experimental data in the required format and run the program without modifying functions or precisely guessing the initial values, or finding the parameters in previous works and will be able to fit the material constants efficiently.Keywords: constitutive equations, FE modelling, MATLAB program, non-linear curve fitting
Procedia PDF Downloads 903452 A Neural Network Modelling Approach for Predicting Permeability from Well Logs Data
Authors: Chico Horacio Jose Sambo
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Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models.Keywords: neural network, permeability, multilayer perceptron, well log
Procedia PDF Downloads 3933451 Artificial Neural Network Based Parameter Prediction of Miniaturized Solid Rocket Motor
Authors: Hao Yan, Xiaobing Zhang
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The working mechanism of miniaturized solid rocket motors (SRMs) is not yet fully understood. It is imperative to explore its unique features. However, there are many disadvantages to using common multi-objective evolutionary algorithms (MOEAs) in predicting the parameters of the miniaturized SRM during its conceptual design phase. Initially, the design variables and objectives are constrained in a lumped parameter model (LPM) of this SRM, which leads to local optima in MOEAs. In addition, MOEAs require a large number of calculations due to their population strategy. Although the calculation time for simulating an LPM just once is usually less than that of a CFD simulation, the number of function evaluations (NFEs) is usually large in MOEAs, which makes the total time cost unacceptably long. Moreover, the accuracy of the LPM is relatively low compared to that of a CFD model due to its assumptions. CFD simulations or experiments are required for comparison and verification of the optimal results obtained by MOEAs with an LPM. The conceptual design phase based on MOEAs is a lengthy process, and its results are not precise enough due to the above shortcomings. An artificial neural network (ANN) based parameter prediction is proposed as a way to reduce time costs and improve prediction accuracy. In this method, an ANN is used to build a surrogate model that is trained with a 3D numerical simulation. In design, the original LPM is replaced by a surrogate model. Each case uses the same MOEAs, in which the calculation time of the two models is compared, and their optimization results are compared with 3D simulation results. Using the surrogate model for the parameter prediction process of the miniaturized SRMs results in a significant increase in computational efficiency and an improvement in prediction accuracy. Thus, the ANN-based surrogate model does provide faster and more accurate parameter prediction for an initial design scheme. Moreover, even when the MOEAs converge to local optima, the time cost of the ANN-based surrogate model is much lower than that of the simplified physical model LPM. This means that designers can save a lot of time during code debugging and parameter tuning in a complex design process. Designers can reduce repeated calculation costs and obtain accurate optimal solutions by combining an ANN-based surrogate model with MOEAs.Keywords: artificial neural network, solid rocket motor, multi-objective evolutionary algorithm, surrogate model
Procedia PDF Downloads 793450 Service Life Modelling of Concrete Deterioration Due to Biogenic Sulphuric Acid (BSA) Attack-State-of-an-Art-Review
Authors: Ankur Bansal, Shashank Bishnoi
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Degradation of Sewage pipes, sewage pumping station and Sewage treatment plants(STP) is of major concern due to difficulty in their maintenance and the high cost of replacement. Most of these systems undergo degradation due to Biogenic sulphuric acid (BSA) attack. Since most of Waste water treatment system are underground, detection of this deterioration remains hidden. This paper presents a literature review, outlining the mechanism of this attack focusing on critical parameters of BSA attack, along with available models and software to predict the deterioration due to this attack. This paper critically examines the various steps and equation in various Models of BSA degradation, detail on assumptions and working of different softwares are also highlighted in this paper. The paper also focuses on the service life design technique available through various codes and method to integrate the servile life design with BSA degradation on concrete. In the end, various methods enhancing the resistance of concrete against Biogenic sulphuric acid attack are highlighted. It may be concluded that the effective modelling for degradation phenomena may bring positive economical and environmental impacts. With current computing capabilities integrated degradation models combining the various durability aspects can bring positive change for sustainable society.Keywords: concrete degradation, modelling, service life, sulphuric acid attack
Procedia PDF Downloads 3033449 Combination of Modelling and Environmental Life Cycle Assessment Approach for Demand Driven Biogas Production
Authors: Juan A. Arzate, Funda C. Ertem, M. Nicolas Cruz-Bournazou, Peter Neubauer, Stefan Junne
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— One of the biggest challenges the world faces today is global warming that is caused by greenhouse gases (GHGs) coming from the combustion of fossil fuels for energy generation. In order to mitigate climate change, the European Union has committed to reducing GHG emissions to 80–95% below the level of the 1990s by the year 2050. Renewable technologies are vital to diminish energy-related GHG emissions. Since water and biomass are limited resources, the largest contributions to renewable energy (RE) systems will have to come from wind and solar power. Nevertheless, high proportions of fluctuating RE will present a number of challenges, especially regarding the need to balance the variable energy demand with the weather dependent fluctuation of energy supply. Therefore, biogas plants in this content would play an important role, since they are easily adaptable. Feedstock availability varies locally or seasonally; however there is a lack of knowledge in how biogas plants should be operated in a stable manner by local feedstock. This problem may be prevented through suitable control strategies. Such strategies require the development of convenient mathematical models, which fairly describe the main processes. Modelling allows us to predict the system behavior of biogas plants when different feedstocks are used with different loading rates. Life cycle assessment (LCA) is a technique for analyzing several sides from evolution of a product till its disposal in an environmental point of view. It is highly recommend to use as a decision making tool. In order to achieve suitable strategies, the combination of a flexible energy generation provided by biogas plants, a secure production process and the maximization of the environmental benefits can be obtained by the combination of process modelling and LCA approaches. For this reason, this study focuses on the biogas plant which flexibly generates required energy from the co-digestion of maize, grass and cattle manure, while emitting the lowest amount of GHG´s. To achieve this goal AMOCO model was combined with LCA. The program was structured in Matlab to simulate any biogas process based on the AMOCO model and combined with the equations necessary to obtain climate change, acidification and eutrophication potentials of the whole production system based on ReCiPe midpoint v.1.06 methodology. Developed simulation was optimized based on real data from operating biogas plants and existing literature research. The results prove that AMOCO model can successfully imitate the system behavior of biogas plants and the necessary time required for the process to adapt in order to generate demanded energy from available feedstock. Combination with LCA approach provided opportunity to keep the resulting emissions from operation at the lowest possible level. This would allow for a prediction of the process, when the feedstock utilization supports the establishment of closed material circles within a smart bio-production grid – under the constraint of minimal drawbacks for the environment and maximal sustainability.Keywords: AMOCO model, GHG emissions, life cycle assessment, modelling
Procedia PDF Downloads 1813448 Establishment of a Nomogram Prediction Model for Postpartum Hemorrhage during Vaginal Delivery
Authors: Yinglisong, Jingge Chen, Jingxuan Chen, Yan Wang, Hui Huang, Jing Zhnag, Qianqian Zhang, Zhenzhen Zhang, Ji Zhang
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Purpose: The study aims to establish a nomogram prediction model for postpartum hemorrhage (PPH) in vaginal delivery. Patients and Methods: Clinical data were retrospectively collected from vaginal delivery patients admitted to a hospital in Zhengzhou, China, from June 1, 2022 - October 31, 2022. Univariate and multivariate logistic regression were used to filter out independent risk factors. A nomogram model was established for PPH in vaginal delivery based on the risk factors coefficient. Bootstrapping was used for internal validation. To assess discrimination and calibration, receiver operator characteristics (ROC) and calibration curves were generated in the derivation and validation groups. Results: A total of 1340 cases of vaginal delivery were enrolled, with 81 (6.04%) having PPH. Logistic regression indicated that history of uterine surgery, induction of labor, duration of first labor, neonatal weight, WBC value (during the first stage of labor), and cervical lacerations were all independent risk factors of hemorrhage (P <0.05). The area-under-curve (AUC) of ROC curves of the derivation group and the validation group were 0.817 and 0.821, respectively, indicating good discrimination. Two calibration curves showed that nomogram prediction and practical results were highly consistent (P = 0.105, P = 0.113). Conclusion: The developed individualized risk prediction nomogram model can assist midwives in recognizing and diagnosing high-risk groups of PPH and initiating early warning to reduce PPH incidence.Keywords: vaginal delivery, postpartum hemorrhage, risk factor, nomogram
Procedia PDF Downloads 623447 Modelling of Induction Motor Including Skew Effect Using MWFA for Performance Improvement
Authors: M. Harir, A. Bendiabdellah, A. Chaouch, N. Benouzza
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This paper deals with the modelling and simulation of the squirrel cage induction motor by taking into account all space harmonic components, as well as the introduction of the bars skew, in the calculation of the linear evolution of the magnetomotive force (MMF) between the slots extremities. The model used is based on multiple coupled circuits and the modified winding function approach (MWFA). The effect of skewing is included in the calculation of motors inductances with an axial asymmetry in the rotor. The simulation results in both time and spectral domains show the effectiveness and merits of the model and the error that may be caused if the skew of the bars is neglected.Keywords: modeling, MWFA, skew effect, squirrel cage induction motor, spectral domain
Procedia PDF Downloads 431