Search results for: predict

Authors: Xavier Lorang, Ahmadali Tahmasebimoradi, Chetra Mang, Sylvain Girard

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The additive manufacturing processes (e.g. selective laser melting) allow us to produce lattice structures which have less weight, higher impact absorption capacity, and better thermal exchange property compared to the classical structures. Unfortunately, geometric imperfections (defects) in the lattice structures are by-products results of the manufacturing process. These imperfections decrease the lifetime and the strength of the lattice structures and alternate their mechanical responses. The objective of the paper is to present a simulation strategy which allows us to take into account the effect of the geometric imperfections on the mechanical response of the lattice structure. In the first part, an identification method of geometric imperfection parameters of the lattice structure based on point clouds is presented. These point clouds are based on tomography measurements. The point clouds are fed into the platform LATANA (LATtice ANAlysis) developed by IRT-SystemX to characterize the geometric imperfections. This is done by projecting the point clouds of each microbeam along the beam axis onto a 2D surface. Then, by fitting an ellipse to the 2D projections of the points, the geometric imperfections are characterized by introducing three parameters of an ellipse; semi-major/minor axes and angle of rotation. With regard to the calculated parameters of the microbeam geometric imperfections, a statistical analysis is carried out to determine a probability density law based on a statistical hypothesis. The microbeam samples are randomly drawn from the density law and are used to generate lattice structures. In the second part, a finite element model for the lattice structure with the simplified geometric imperfections (ellipse parameters) is presented. This numerical model is used to simulate the generated lattice structures. The propagation of the uncertainties of geometric imperfections is shown through the distribution of the computed mechanical responses of the lattice structures.

Keywords: additive manufacturing, finite element model, geometric imperfections, lattice structures, propagation of uncertainty

Procedia PDF Downloads 171

Authors: Walid A. Attia, Taher M. Bazan, Fawzy Eltohamy, Mahmoud Fathy

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System performance evaluation is an essential stage in the design of high-resolution satellite telescopes prior to the development process. In this paper, a system performance evaluation of a very high-resolution satellite telescope is investigated. The evaluated system has a Korsch optical scheme design. This design has been discussed in another paper with respect to three-mirror anastigmat (TMA) scheme design and the former configuration showed better results. The investigated system is based on the Korsch optical design integrated with a time-delay and integration charge coupled device (TDI-CCD) sensor to achieve a ground sampling distance (GSD) of 25 cm. The key performance metrics considered are the spatial resolution, the signal to noise ratio (SNR) and the total modulation transfer function (MTF) of the system. In addition, the national image interpretability rating scale (NIIRS) metric is assessed to predict the image quality according to the modified general image quality equation (GIQE). Based on the orbital, optical and detector parameters, the estimated GSD is found to be 25 cm. The SNR has been analyzed at different illumination conditions of target albedos, sun and sensor angles. The system MTF has been computed including diffraction, aberration, optical manufacturing, smear and detector sampling as the main contributors for evaluation the MTF. Finally, the system performance evaluation results show that the computed MTF value is found to be around 0.08 at the Nyquist frequency, the SNR value was found to be 130 at albedo 0.2 with a nadir viewing angles and the predicted NIIRS is in the order of 6.5 which implies a very good system image quality.

Keywords: modulation transfer function, national image interpretability rating scale, signal to noise ratio, satellite telescope performance evaluation

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Authors: Ahmet Koyuncu, Abdullah Berkan Erdogmus, Orkun Dogu, Sinan Uygur

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Thermoelectric modules are integrated to electronic components to keep their temperature in specific values in electronic cooling applications. They can be used in different ambient temperatures. The cold side temperatures of thermoelectric modules depend on their hot side temperatures, operation currents, and heat loads. Performance curves of thermoelectric modules are given at most two different hot surface temperatures in product catalogs. Characteristic properties are required to select appropriate thermoelectric modules in thermal design phase of projects. Generally, manufacturers do not provide characteristic material property values of thermoelectric modules to customers for confidentiality. Common commercial software applied like ANSYS ICEPAK, FloEFD, etc., include thermoelectric modules in their libraries. Therefore, they can be easily used to predict the effect of thermoelectric usage in thermal design. Some software requires only the performance values in different temperatures. However, others like ICEPAK require three temperature-dependent equations for material properties (Seebeck coefficient (α), electrical resistivity (β), and thermal conductivity (γ)). Since the number and the variety of thermoelectric modules are limited in this software, definitions of characteristic material properties of thermoelectric modules could be required. In this manuscript, the method of derivation of characteristic material properties from the datasheet of thermoelectric modules is presented. Material characteristics were estimated from two different performance curves by experimentally and numerically in this study. Numerical calculations are accomplished in ICEPAK by using a thermoelectric module exists in the ICEPAK library. A new experimental setup was established to perform experimental study. Because of similar results of numerical and experimental studies, it can be said that proposed equations are approved. This approximation can be suggested for the analysis includes different type or brand of TEC modules.

Keywords: electrical resistivity, material characteristics, thermal conductivity, thermoelectric coolers, seebeck coefficient

Procedia PDF Downloads 166

Authors: N. Alias, W. Z. W. Muhammad, M. N. M. Ibrahim, M. Mohamed, H. F. S. Saipol, U. N. Z. Ariffin, N. A. Zakaria, M. S. Z. Suardi

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This paper presents the performance comparison of some computation software for solving the boundary element method (BEM). BEM formulation is the numerical technique and high potential for solving the advance mathematical modeling to predict the production of oil well in arbitrarily shaped based on multiple leases reservoir. The limitation of data validation for ensuring that a program meets the accuracy of the mathematical modeling is considered as the research motivation of this paper. Thus, based on this limitation, there are three steps involved to validate the accuracy of the oil production simulation process. In the first step, identify the mathematical modeling based on partial differential equation (PDE) with Poisson-elliptic type to perform the BEM discretization. In the second step, implement the simulation of the 2D BEM discretization using COMSOL Multiphysic and MATLAB programming languages. In the last step, analyze the numerical performance indicators for both programming languages by using the validation of Fortran programming. The performance comparisons of numerical analysis are investigated in terms of percentage error, comparison graph and 2D visualization of pressure on oil production of multiple leases reservoir. According to the performance comparison, the structured programming in Fortran programming is the alternative software for implementing the accurate numerical simulation of BEM. As a conclusion, high-level language for numerical computation and numerical performance evaluation are satisfied to prove that Fortran is well suited for capturing the visualization of the production of oil well in arbitrarily shaped.

Keywords: performance comparison, 2D visualization, COMSOL multiphysic, MATLAB, Fortran, modelling and simulation, boundary element method, reservoir pressure

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Authors: J. O. Idoko, C. N. Michael, T. O. Fasuan

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This study investigated the effects of cooking time, seed-to-water ratio and soaking time on proximate and functional properties of African walnut seed using Box-Behnken design and Response Surface Methodology (BBD-RSM) with a view to increase its utilization in the food industry. African walnut seeds were sorted washed, soaked, cooked, dehulled, sliced, dried and milled. Proximate analysis and functional properties of the samples were evaluated using standard procedures. Data obtained were analyzed using descriptive and inferential statistics. Quadratic models were obtained to predict the proximate and functional qualities as a function of cooking time, seed-to-water ratio and soaking time. The results showed that the crude protein ranged between 11.80% and 23.50%, moisture content ranged between 1.00% and 4.66%, ash content ranged between 3.35% and 5.25%, crude fibre ranged from 0.10% to 7.25% and carbohydrate ranged from 1.22% to 29.35%. The functional properties showed that soluble protein ranged from 16.26% to 42.96%, viscosity ranged from 23.43 mPas to 57 mPas, emulsifying capacity ranged from 17.14% to 39.43% and water absorption capacity ranged from 232% to 297%. An increase in the volume of water used during cooking resulted in loss of water soluble protein through leaching, the length of soaking time and the moisture content of the dried product are inversely related, ash content is inversely related to the cooking time and amount of water used, extraction of fat is enhanced by increase in soaking time while increase in cooking and soaking times result into decrease in fibre content. The results obtained indicated that African walnut could be used in several food formulations as protein supplement and binder.

Keywords: African walnut, functional properties, proximate analysis, response surface methodology

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Authors: Redvan Ghasemlounia, Elham Ansari, Hikmet Kerem Cigizoglu

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Hydrological modeling in arid and semi-arid regions is very important. Iran has many regions with these climate conditions such as Chaharmahal and Bakhtiari province that needs lots of attention with an appropriate management. Forecasting of hydrological parameters and estimation of hydrological events of catchments, provide important information that used for design, management and operation of water resources such as river systems, and dams, widely. Discharge in rivers is one of these parameters. This study presents the application and comparison of some estimation methods such as Feed-Forward Back Propagation Neural Network (FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) to predict the daily flow discharge of the Soolegan River, located at Chaharmahal and Bakhtiari province, in Iran. In this study, Soolegan, station was considered. This Station is located in Soolegan River at 51° 14՜ Latitude 31° 38՜ longitude at North Karoon basin. The Soolegan station is 2086 meters higher than sea level. The data used in this study are daily discharge and daily precipitation of Soolegan station. Feed Forward Back Propagation Neural Network(FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) models were developed using the same input parameters for Soolegan's daily discharge estimation. The results of estimation models were compared with observed discharge values to evaluate performance of the developed models. Results of all methods were compared and shown in tables and charts.

Keywords: ANN, multi linear regression, Bayesian network, forecasting, discharge, gene expression programming

Procedia PDF Downloads 549

Authors: Tettey Afi Pamela, Sotaro Fujii, Hidetoshi Saito, Kawaii Koichiro

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Introduction: Organisms employ adaptive mechanisms when faced with any stressor or risk of being wiped out. This has made it possible for them to survive in harsh environmental conditions such as increasing temperature, low pH, and anoxia. Some of the mechanisms they utilize include the expression of heat shock proteins, synthesis of cryoprotectants, and anhydrobiosis. Heat shock proteins (HSPs) have been widely studied to determine their involvement in stress tolerance among various organism, of which chironomid species have been no exception. We examined the survival and expression of genes encoding five (5) heat shock proteins (HSP70, HSP67, HSP60, HSP27, and HSP23) from Chironomus sulfurosus larvae reared from 1st instar at 25°C, 30°C, 35°C, and 40°C. Results: The highest survival rate was recorded at 30°C, followed by 25°C, then 35°C. Only a small percentage of C. sulfurosus survived at 40°C (14.5%). With regards to HSPs expression, some HSPs responded to an increase in high temperature. The relative expression levels were lowest at 30°C for HSP70, HSP60, HSP27, and HSP23. At 25°C and 40°C, HSP70, HSP67, HSP60, HSP27, and HSP23 had the highest expression. At 35°C, all had the lowest expression. Discussion: The expression of heat shock proteins varies from one species to another. We designated the genes HSP 70, HSP 67, HSP 60, HSP 27, and HSP 23 genes based on transcriptome analysis of C. sulfurosus. Our study can be termed as a long-heat shock study as C. sulfurosus was reared from the first instar to the fourth instar, and this might have led to a continuous induction of HSPs at 25°C. 40°C had the lowest survival but highest HSPs expression as C. sulfurosus larvae had to utilize HSPs for sustenance. These results and future high-throughput studies at both the transcriptome and proteome level will improve the information needed to predict the future geographic distribution of these species within the context of global warming.

Keywords: chironomid, heat shock proteins, high temperature, heat shock protein expression

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Authors: Geno Paul S. Cumla, Jan Mariel M. Gentiles, M. Brenda Gajelan-Samson

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Eutrophication is a process where in there is a surplus of nutrients present in a lake or river. Harmful cyanobacteria, hypoxia, and primarily algae, which contain toxins, grow because of the excess nutrients. Algal blooms can cause fish kills, limiting the light penetration which reduces growth of aquatic organisms, causing die-offs of plants and produce conditions that are dangerous to aquatic and human life. The main cause for eutrophication is the presence of excessive amounts of phosphorus (P) and nitrogen (N). Nitrogen is necessary for the production of the plant tissues and is usually used to synthesize proteins. Nitrate is a compound that contains nitrogen, and at elevated levels it can cause harmful effects. Excessive amounts of phosphorus, displaced through human activity, is the major cause of algae growth and as well as degraded water quality. To accomplish this study the Assessment of Soluble inorganic nitrogen (SIN), Assessment of Soluble reactive phosphate (SRP), Determination of Chlorophyll a (Chl-a) concentration, and Determination of Dominating Taxa were done. The study addresses the high probability of algal blooms in Maqueda Bay by assessing the biotic and abiotic factors of Antiao and Jiabong rivers. The data predicts the overgrowth of algae and to create awareness to prevent the event from taking place. The study assesses the adverse effects that could be prevented by understanding and controlling algae. This should predict future cases of algal blooms and allow government agencies which require data to create programs to prevent and assess these issues.

Keywords: eutrophication, chlorophyll a, nitrogen, phosphorus, red tide, Kjeldahl method, spectrophotometer, assessment of soluble inorganic nitrogen, SIN, assessment of soluble reactive phosphate, SRP

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Authors: Kyle Burke Pfeiffer, Carmella Burdi, Karen Marsh

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The evacuation and shelter-in-place planning process employed by most jurisdictions within the United States is not informed by a scientifically-derived framework that is inclusive of the behavioral and policy-related indicators of public compliance with evacuation orders. While a significant body of work exists to define these indicators, the research findings have not been well-integrated nor translated into useable planning factors for public safety officials. Additionally, refinement of the planning factors alone is insufficient to support science-based evacuation planning as the behavioral elements of evacuees—even with consideration of policy-related indicators—must be examined in the context of specific regional transportation and shelter networks. To address this problem, the Federal Emergency Management Agency and Argonne National Laboratory developed an agent-based model to support regional analysis of zone-based evacuation in southeastern Georgia. In particular, this model allows public safety officials to analyze the consequences that a range of hazards may have upon a community, assess evacuation and shelter-in-place decisions in the context of specified evacuation and response plans, and predict outcomes based on community compliance with orders and the capacity of the regional (to include extra-jurisdictional) transportation and shelter networks. The intention is to use this model to aid evacuation planning and decision-making. Applications for the model include developing a science-driven risk communication strategy and, ultimately, in the case of evacuation, the shortest possible travel distance and clearance times for evacuees within the regional boundary conditions.

Keywords: agent-based modeling for evacuation, decision-support for evacuation planning, evacuation planning, human behavior in evacuation

Procedia PDF Downloads 215

Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

Procedia PDF Downloads 282

Authors: Mahzabin Afroz, Indubhushan Patnaikuni, Srikanth Venkatesan

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It is well-known that fly ash can be used in high volume as a partial replacement of cement to get beneficial effects on concrete. High volume fly ash (HVFA) concrete is currently emerging as a popular option to strengthen by fiber. Although studies have supported the use of fibers with fly ash, a unified model along with the incorporation into finite element software package to estimate the maximum flexural loads need to be developed. In this study, nonlinear finite element analysis of steel fiber reinforced high strength HVFA concrete beam under static loadings was conducted to investigate their failure modes in terms of ultimate load. First of all, the experimental investigation of mechanical properties of high strength HVFA concrete was done and validates with developed numerical model with the appropriate modeling of element size and mesh by ANSYS 16.2. To model the fiber within the concrete, three-dimensional random fiber distribution was simulated by spherical coordinate system. Three types of high strength HVFA concrete beams were analyzed reinforced with 0.5, 1 and 1.5% volume fractions of steel fibers with specific mechanical and physical properties. The result reveals that the use of nonlinear finite element analysis technique and three-dimensional random fiber orientation exhibited fairly good agreement with the experimental results of flexural strength, load deflection and crack propagation mechanism. By utilizing this improved model, it is possible to determine the flexural behavior of different types and proportions of steel fiber reinforced HVFA concrete beam under static load. So, this paper has the originality to predict the flexural properties of steel fiber reinforced high strength HVFA concrete by numerical simulations.

Keywords: finite element analysis, high volume fly ash, steel fibers, spherical coordinate system

Procedia PDF Downloads 121

Authors: Jantakan Jullawateelert, Korakod Haonoo, Sutipong Sananseang, Sarun Torpaiboon, Thanunthon Bowornsakulwong, Lalintip Hocharoen

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Process scale-up is essential for the biological process to increase production capacity from bench-scale bioreactors to either pilot or commercial production. Scale-up based on constant volumetric oxygen mass transfer coefficient (KLa) is mostly used as a scale-up factor since oxygen supply is one of the key limiting factors for cell growth. However, to estimate KLa of culture vessels operated with different conditions are time-consuming since it is considerably influenced by a lot of factors. To overcome the issue, this study aimed to establish correlation equations of KLa and operating parameters in 0.5 L and 5 L bioreactor employed with pitched-blade impeller and gas sparger. Temperature, gas flow rate, agitation speed, and impeller position were selected as process parameters and equations were created using response surface methodology (RSM) based on central composite design (CCD). In addition, the effects of these parameters on KLa were also investigated. Based on RSM, second-order polynomial models for 0.5 L and 5 L bioreactor were obtained with an acceptable determination coefficient (R²) as 0.9736 and 0.9190, respectively. These models were validated, and experimental values showed differences less than 10% from the predicted values. Moreover, RSM revealed that gas flow rate is the most significant parameter while temperature and agitation speed were also found to greatly affect the KLa in both bioreactors. Nevertheless, impeller position was shown to influence KLa in only 5L system. To sum up, these modeled correlations can be used to accurately predict KLa within the specified range of process parameters of two different sizes of bioreactors for further scale-up application.

Keywords: response surface methodology, scale-up, stirred-tank bioreactor, volumetric oxygen mass transfer coefficient

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Authors: Rebecca J. Hafner, Daniel Read, David Elmes

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The current research applies decision making theory in order to address the problem of increasing uptake of energy-efficient technologies in the market place, where uptake is currently slower than one might predict following rational choice models. Specifically, in two studies we apply the alignable/non-alignable features effect and explore the impact of varying information structure on the consumers’ preference for standard versus energy efficient technologies. As researchers in the Interdisciplinary centre for Storage, Transformation and Upgrading of Thermal Energy (i-STUTE) are currently developing energy efficient heating systems for homes and businesses, we focus on the context of home heating choice, and compare preference for a standard condensing boiler versus an energy efficient heat pump, according to experimental manipulations in the structure of prior information. In Study 1, we find that people prefer stronger alignable features when options are similar; an effect which is mediated by an increased tendency to infer missing information is the same. Yet, in contrast to previous research, we find no effects of alignability on option preference when options differ. The advanced methodological approach used here, which is the first study of its kind to randomly allocate features as either alignable or non-alignable, highlights potential design effects in previous work. Study 2 is designed to explore the interaction between alignability and construal level as an explanation for the shift in attentional focus when options differ. Theoretical and applied implications for promoting energy efficient technologies are discussed.

Keywords: energy-efficient technologies, decision-making, alignability effects, construal level theory, CO2 reduction

Procedia PDF Downloads 316

Authors: T. Srinivasa Krishna, K. Narendra, K. Thomas, S. S. Raju, B. Munibhadrayya

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The densities, speeds of sound and refractive index of the binary mixture of ionic liquid (IL) 1-Butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([BMIM][Imide]) and Pyrrolidin-2-one(PY) was measured at atmospheric pressure, and over the range of temperatures T= (298.15 -323.15)K. The excess molar volume, excess isentropic compressibility, excess speed of sound, partial molar volumes, and isentropic partial molar compressibility were calculated from the values of the experimental density and speed of sound. From the experimental data excess thermal expansion coefficients and isothermal pressure coefficient of excess molar enthalpy at 298.15K were calculated. The results were analyzed and were discussed from the point of view of structural changes. Excess properties were calculated and correlated by the Redlich–Kister and the Legendre polynomial equation and binary coefficients were obtained. Values of excess partial volumes at infinite dilution for the binary system at different temperatures were calculated from the adjustable parameters obtained from Legendre polynomial and Redlich–Kister smoothing equation. Deviation in refractive indices ΔnD and deviation in molar refraction, ΔRm were calculated from the measured refractive index values. Equations of state and several mixing rules were used to predict refractive indices of the binary mixtures and compared with the experimental values by means of the standard deviation and found to be in excellent agreement. By using Prigogine–Flory–Patterson (PFP) theory, the above thermodynamic mixing functions have been calculated and the results obtained from this theory were compared with experimental results.

Keywords: density, refractive index, speeds of sound, Prigogine-Flory-Patterson theory

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Authors: Florina Magdalena Anichitoae, Anca Dobrean, Ionut Stelian Florean

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Due to the fact that cyberbullying victimization is an increasing problem nowadays, affecting more and more children and adolescents around the world, we wanted to take a step forward analyzing this phenomenon. So, we took a look at some variables which haven't been studied together before, trying to develop another way to view cyberbullying victimization. We wanted to test the effects of the mother, father, and peer attachment on adolescent involvement in cyberbullying as victims through unconditional self acceptance. Furthermore, we analyzed each subscale of the IPPA-R, the instrument we have used for parents and peer attachment measurement, in regards to cyberbullying victimization through unconditional self acceptance. We have also analyzed if gender and age could be taken into consideration as moderators in this model. The analysis has been performed on 653 adolescents aged 11-17 years old from Romania. We used structural equation modeling, working in R program. For the fidelity analysis of the IPPA-R subscales, USAQ, and Cyberbullying Test, we have calculated the internal consistency index, which varies between .68-.91. We have created 2 models: the first model including peer alienation, peer trust, peer communication, self acceptance and cyberbullying victimization, having CFI=0.97, RMSEA=0.02, 90%CI [0.02, 0.03] and SRMR=0.07, and the second model including parental alienation, parental trust, parental communication, self acceptance and cyberbullying victimization and had CFI=0.97, RMSEA=0.02, 90%CI [0.02, 0.03] and SRMR=0.07. Our results were interesting: on one hand, cyberbullying victimization is predicted by peer alienation and peer communication through unconditional self acceptance. Peer trust directly, significantly, and negatively predicted the implication in cyberbullying. In this regard, considering gender and age as moderators, we found that the relationship between unconditional self acceptance and cyberbullying victimization is stronger in girls, but age does not moderate the relationship between unconditional self acceptance and cyberbullying victimization. On the other hand, regarding the degree of cyberbullying victimization as being predicted through unconditional self acceptance by parental alienation, parental communication, and parental trust, this hypothesis was not supported. Still, we could identify a direct path to positively predict victimization through parental alienation and negatively through parental trust. There are also some limitations to this study, which we've discussed in the end.

Keywords: adolescent, attachment, cyberbullying victimization, parents, peers, unconditional self-acceptance

Procedia PDF Downloads 186

Authors: Adetokunbo Ademola Falade, Oluwatoyin Olakunle Akinsete, Hussein Omeiza Aliu

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Solvent-aided dilution processes are often employed to recover bitumen by reducing its viscosity. In this study, methanol, toluene, and xylene were investigated as potential hydrocarbon solvents for solvent-aided hydrocarbon recovery of Agbabu bitumen. Solubility, Viscosity, and Saturate, Aromatic, Resin and Asphaltene (SARA) Analysis tests were carried out to determine the solubility of the bitumen in the solvents, the viscosity, and the SARA fraction of the natural bitumen and bitumen-solvent mixtures. Agbabu bitumen was found to have a high content of saturates and aromatics. Viscosity decreases as pressure increases, while solubility reduces as temperature increases. The experimental diffusivity of the sample decreases with temperature and increases with pressure, indicating that the presence of additional solvent molecules in the oil phase facilitates diffusion. Agbabu bitumen was found to be most soluble in toluene, and its viscosity was reduced most in it. Xylene exhibited a similar effect as toluene on the sample, though lesser but better than methanol. Methanol reduced the saturated content and significantly raised the asphaltene content, keeping the mixture viscosity high, a condition that, in turn, favors its colloidal stability. The colloidal instability index (CII) values, which account for the asphaltene stability of the mixture, show that the bitumen-methanol system with a CII of 0.874 will have mild asphaltene deposit issues while others are unstable. This approach of combining multiple tests with the CII can accurately predict the behavior of Agbabu bitumen in solvents and enhance the decision on the choice of bitumen recovery technology.

Keywords: asphaltene, bitumen, diffusivity, hydrocarbon solvent, SARA

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Authors: Chongyang Ye, Rong Liu

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Elastic compression stockings (ECSs) have been widely applied in prophylaxis and treatment of chronic venous insufficiency of lower extremities. The medical function of ECS is to improve venous return and increase muscular pumping action to facilitate blood circulation, which is largely determined by the complex interaction between the ECS and lower limb tissues. Understanding the mechanical transmission of ECS along the skin surface, deeper tissues, and vascular system is essential to assess the effectiveness of the ECSs. In this study, a three-dimensional (3D) finite element (FE) model of the leg-ECS system integrated with a 3D fluid-solid interaction (FSI) model of the leg-vein system was constructed to analyze the biomechanical properties of veins and soft tissues under different ECS compression. The Magnetic Resonance Imaging (MRI) of the human leg was divided into three regions, including soft tissues, bones (tibia and fibula) and veins (peroneal vein, great saphenous vein, and small saphenous vein). The ECSs with pressure ranges from 15 to 26 mmHg (Classes I and II) were adopted in the developed FE-FSI model. The soft tissue was assumed as a Neo-Hookean hyperelastic model with the fixed bones, and the ECSs were regarded as an orthotropic elastic shell. The interfacial pressure and stress transmission were simulated by the FE model, and venous hemodynamics properties were simulated by the FSI model. The experimental validation indicated that the simulated interfacial pressure distributions were in accordance with the pressure measurement results. The developed model can be used to predict interfacial pressure, stress transmission, and venous hemodynamics exerted by ECSs and optimize the structure and materials properties of ECSs design, thus improving the efficiency of compression therapy.

Keywords: elastic compression stockings, fluid-solid interaction, tissue and vein properties, prediction

Procedia PDF Downloads 101

Authors: E. Mattio

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The aim of this project is to study the Chinese presence in Ethiopia, following the path of the last salt caravans from Danakil to Tigray region. Official estimates of the number of Chinese in Africa vary widely; on the continent, there are increasingly diverse groups of Chinese migrants in terms of language, dialect, class, education, and employment. Based on this and on a very general state of the art, it was decided to increase the studies on this phenomenon, documenting the extraction of salt and following the sellers in the north of the country. The project is unique and allows you to admire a landscape that will soon change, due to the construction of infrastructure that is changing the dynamics of movement and sales. To carry out this study, interdisciplinary investigation methods were integrated, such as landscape archeology, historiographic research, participatory anthropology, geopolitics, and cultural anthropology and ethnology. There are two main objectives of the research. The first was an analysis of risk perceptions to predict what will happen to these populations and how the territory will be modified, trying to monitor the growth of infrastructure in the country and the effects it will have on the population. Thanks to the use of GIS, some roads created by Chinese companies that worked in the area have been georeferenced. The second point was to document the life and rituals of Ethiopian populations, in order not to lose the aspects of uniqueness that risk being lost. The local interviews have garnered impressions and criticisms from the local population to understand whether the Chinese presence is perceived as a threat or a solution. Among the most exclusive interviews, there are those made to Afar leaders in the Logya area and some Coptic representatives in the Wukro area. To make this project even more unique, the Coptic rituals of Gennà and Timkat have been documented, unique expressions of a millennial tradition. The aim was to understand whether the Maoist presence began to influence the religious rites and forms of belief present in the country.

Keywords: China, Ethiopia, GIS, risk perceptions

Procedia PDF Downloads 137

Authors: Shiran Bord, Assaf Oshri, Matthew W. Carlson, Sihong Liu

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Background: Alcohol-impaired driving (AID) and binge drinking are major health concerns among college students. Although the link between binge drinking and AID is well established, knowledge regarding binge drinking patterns, the factors influencing binge drinking, and the associations between consumption patterns and alcohol-related risk behaviors is lacking. Aims: To examine heterogeneous trajectories of binge drinking during college and tests factors that might predict class membership as well as class membership outcomes. Methods: Data were obtained from a sample of 1,265 college students (Mage = 18.5, SD = .66) as part of the Longitudinal Study of Violence Against Women (N = 1,265; 59.3% female; 69.2% white). Analyses were completed in three stages. First, a growth curve analysis was conducted to identify trajectories of binge drinking over time. Second, growth curve mixture modeling analyses were pursued to assess unobserved growth trajectories of binge drinking without predictors. Lastly, parental drinking variables were added to the model as predictors of class membership, and AID and being a passenger of a drunk driver were added to the model as outcomes. Results: Three binge drinking trajectories were identified: high-convex, medium concave and low-increasing. Parental drinking was associated with being in high-convex and medium-concave classes. Compared to the low-increasing class, the high convex and medium concave classes reported more AID and being a passenger of a drunk driver more frequently. Conclusions: Parental drinking may affect children’s later engagement in AID. Efforts should focus on parents' education regarding the consequences of parental modeling of alcohol consumption.

Keywords: alcohol impaired driving, alcohol consumption, binge drinking, college students, parental modeling

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Authors: David Alejandro Lazo-Vasquez, Jorge Luis Balino

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In the petroleum industry, multiphase flow occurs when oil, gas, and water are transported in the same pipe through large pipeline systems. The flow can take different patterns depending on parameters like fluid velocities, pipe diameter, pipe inclination, and fluid properties. Mainly, intermittent flow is produced by the natural propagation of short and long waves, according to the Kelvin-Helmholtz Stability Theory. To model stratified flow and the onset of intermittent flow, it is crucial to have knowledge of short and long waves behavior. The two-fluid model, frequently employed for characterizing multiphase systems, becomes ill-posed for high liquid and gas velocities and large inclination angles, for short waves can develop infinite growth rates. We are interested in focusing attention on long-wave instability, which leads to the production of roll waves that may grow and result in the transition from stratified flow to intermittent flow. In this study, global and local linear stability analyses for dynamic and kinematic stability criteria predict the regions of stability of the flow for different pipe inclinations and fluid velocities in regularized and non-regularized systems, concurrently. It was possible to distinguish when: wave growth rates are absolutely bounded (stable stratified smooth flow), waves have finite growth rates (unstable stratified wavy flow), and when the equation system becomes elliptic and hyperbolization is needed. In order to bound short wave growth rates and regularize the equation system, we incorporated some lower and higher-order terms like interfacial drag and surface tension, respectively.

Keywords: linear stability analysis, multiphase flow, onset of slugging, two-fluid model regularization

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Authors: Konstantinos G. Kostinakis, Manthos K. Papadopoulos, Asimina M. Athanatopoulou

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An important area of research in seismic risk analysis is the evaluation of expected seismic damage of structures under a specific earthquake ground motion. Several conventional intensity measures of ground motion have been used to estimate their damage potential to structures. Yet, none of them was proved to be able to predict adequately the seismic damage of any structural system. Therefore, alternative advanced intensity measures which take into account not only ground motion characteristics but also structural information have been proposed. The adequacy of a number of advanced earthquake intensity measures in prediction of structural damage of 3D R/C buildings under seismic excitation which attacks the building with arbitrary incident angle is investigated in the present paper. To achieve this purpose, a symmetric in plan and an asymmetric 5-story R/C building are studied. The two buildings are subjected to 20 bidirectional earthquake ground motions. The two horizontal accelerograms of each ground motion are applied along horizontal orthogonal axes forming 72 different angles with the structural axes. The response is computed by non-linear time history analysis. The structural damage is expressed in terms of the maximum interstory drift as well as the overall structural damage index. The values of the aforementioned seismic damage measures determined for incident angle 0° as well as their maximum values over all seismic incident angles are correlated with 9 structure-specific ground motion intensity measures. The research identified certain intensity measures which exhibited strong correlation with the seismic damage of the two buildings. However, their adequacy for estimation of the structural damage depends on the response parameter adopted. Furthermore, it was confirmed that the widely used spectral acceleration at the fundamental period of the structure is a good indicator of the expected earthquake damage level.

Keywords: damage indices, non-linear response, seismic excitation angle, structure-specific intensity measures

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Authors: Sanjal Gavande, Thomas Mazzuchi, Shahram Sarkani

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In a complex aerospace operational environment, identifying failures in a procedure involving multiple human-machine interactions are difficult. These failures could lead to accidents causing loss of hardware or human life. The likelihood of failure further increases if operational procedures are tested for a novel system with multiple human-machine interfaces and with no prior performance data. The existing approach in the literature of reviewing complex operational tasks in a flowchart or tabular form doesn’t provide any insight into potential system failures due to human decision-making ability. To address these challenges, this research explores an agent-based simulation approach for reliability assessment and fault detection in complex human-machine systems while utilizing a human decision-making model. The simulation will predict the emergent behavior of the system due to the interaction between humans and their decision-making capability with the varying states of the machine and vice-versa. Overall system reliability will be evaluated based on a defined set of success-criteria conditions and the number of recorded failures over an assigned limit of Monte Carlo runs. The study also aims at identifying high-likelihood failure locations for the system. The research concludes that system reliability and failures can be effectively calculated when individual human and machine agent states are clearly defined. This research is limited to the operations phase of a system lifecycle process in an aerospace environment only. Further exploration of the proposed agent-based and human decision-making model will be required to allow for a greater understanding of this topic for application outside of the operations domain.

Keywords: agent-based model, complex human-machine system, human decision-making model, system reliability assessment

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Authors: Tengqin Han

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Delinquency has been a crucial factor in economics throughout the years. Commonly seen in credit card and mortgage, it played one of the crucial roles in causing the most recent financial crisis in 2008. In each case, a delinquency is a sign of the loaner being unable to pay off the debt, and thus may cause a lost of property in the end. Individually, one case of delinquency seems unimportant compared to the entire credit system. China, as an emerging economic entity, the national strength and economic strength has grown rapidly, and the gross domestic product (GDP) growth rate has remained as high as 8% in the past decades. However, potential risks exist behind the appearance of prosperity. Among the risks, the credit system is the most significant one. Due to long term and a large amount of balance of the mortgage, it is critical to monitor the risk during the performance period. In this project, about 300,000 mortgage account data are analyzed in order to develop a predictive model to predict the probability of delinquency. Through univariate analysis, the data is cleaned up, and through bivariate analysis, the variables with strong predictive power are detected. The project is divided into two parts. In the first part, the analysis data of 2005 are split into 2 parts, 60% for model development, and 40% for in-time model validation. The KS of model development is 31, and the KS for in-time validation is 31, indicating the model is stable. In addition, the model is further validation by out-of-time validation, which uses 40% of 2006 data, and KS is 33. This indicates the model is still stable and robust. In the second part, the model is improved by the addition of macroeconomic economic indexes, including GDP, consumer price index, unemployment rate, inflation rate, etc. The data of 2005 to 2010 is used for model development and validation. Compared with the base model (without microeconomic variables), KS is increased from 41 to 44, indicating that the macroeconomic variables can be used to improve the separation power of the model, and make the prediction more accurate.

Keywords: delinquency, mortgage, model development, model validation

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Authors: Hoyeon Park, Kyoung-jae Kim

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The product recommendation is a field of research that has received much attention in the recent information overload phenomenon. The proliferation of the mobile environment and social media cannot help but affect the results of the recommendation depending on how the factors of the user's situation are reflected in the recommendation process. Recently, research has been spreading attention to the context-aware recommender system which is to reflect user's contextual information in the recommendation process. However, until now, most of the context-aware recommender system researches have been limited in that they reflect the passive context of users. It is expected that the user will be able to express his/her contextual information through his/her active behavior and the importance of the context-aware recommender system reflecting this information can be increased. The purpose of this study is to propose a context-aware recommender system that can reflect the user's emotional state as an active context information to recommendation process. The context-aware recommender system is a recommender system that can make more sophisticated recommendations by utilizing the user's contextual information and has an advantage that the user's emotional factor can be considered as compared with the existing recommender systems. In this study, we propose a method to infer the user's emotional state, which is one of the user's context information, by using the user's facial expression data and to reflect it on the recommendation process. This study collects the facial expression data of a user who is looking at a specific product and the user's product preference score. Then, we classify the facial expression data into several categories according to the previous research and construct a model that can predict them. Next, the predicted results are applied to existing collaborative filtering with contextual information. As a result of the study, it was shown that the recommended results of the context-aware recommender system including facial expression information show improved results in terms of recommendation performance. Based on the results of this study, it is expected that future research will be conducted on recommender system reflecting various contextual information.

Keywords: context-aware, emotional state, recommender systems, business analytics

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Authors: Sirajo Abubakar, Ismail Ahmad Abir, Muhammad Sabiu Bala, Muhammad Mustapha Adejo, Aravind Shanmugaveloo

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Over the years, land subsidence has been a serious threat mostly to urban areas. Land subsidence is the sudden sinking or gradual downward settling of the ground’s surface with little or no horizontal motion. In most areas, land subsidence is a slow process that covers a large area; therefore, it is sometimes left unnoticed. South of Johor is the area of interest for this project because it is going through rapid urbanization. The objective of this research is to evaluate and identify potential deformations in the south of Johor using integrated remote sensing and 2D resistivity methods. Synthetic aperture radar interferometry (InSAR) which is a remote sensing technique has the potential to map coherent displacements at centimeter to millimeter resolutions. Persistent scatterer interferometry (PSI) stacking technique was applied to Sentinel-1 data to detect the earth deformation in the study area. A dipole-dipole configuration resistivity profiling was conducted in three areas to determine the subsurface features in that area. This subsurface features interpreted were then correlated with the remote sensing technique to predict the possible causes of subsidence and uplifts in the south of Johor. Based on the results obtained, West Johor Bahru (0.63mm/year) and Ulu Tiram (1.61mm/year) are going through uplift due to possible geological uplift. On the other end, East Johor Bahru (-0.26mm/year) and Senai (-1.16mm/year) undergo subsidence due to possible fracture and granitic boulders loading. Land subsidence must be taken seriously as it can cause serious damages to infrastructures and human life. Monitoring land subsidence and taking preventive actions must be done to prevent any disasters.

Keywords: interferometric synthetic aperture radar, persistent scatter, minimum spanning tree, resistivity, subsidence

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Authors: G. Ravindranath, S. Savitha

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This paper presents heat transfer analysis of single horizontal bare tube and heat transfer analysis of staggered arrangement of bare tube bundles bare tube bundles in gas-solid (air-solid) fluidized bed and predictions are done by using Artificial Neural Network (ANN) based on experimental data. Fluidized bed provide nearly isothermal environment with high heat transfer rate to submerged objects i.e. due to through mixing and large contact area between the gas and the particle, a fully fluidized bed has little temperature variation and gas leaves at a temperature which is close to that of the bed. Measurement of average heat transfer coefficient was made by local thermal simulation technique in a cold bubbling air-fluidized bed of size 0.305 m. x 0.305 m. Studies were conducted for single horizontal Bare Tube of length 305mm and 28.6mm outer diameter and for bare tube bundles of staggered arrangement using beds of large (average particle diameter greater than 1 mm) particle (raagi and mustard). Within the range of experimental conditions influence of bed particle diameter ( Dp), Fluidizing Velocity (U) were studied, which are significant parameters affecting heat transfer. Artificial Neural Networks (ANNs) have been receiving an increasing attention for simulating engineering systems due to some interesting characteristics such as learning capability, fault tolerance, and non-linearity. Here, feed-forward architecture and trained by back-propagation technique is adopted to predict heat transfer analysis found from experimental results. The ANN is designed to suit the present system which has 3 inputs and 2 out puts. The network predictions are found to be in very good agreement with the experimental observed values of bare heat transfer coefficient (hb) and nusselt number of bare tube (Nub).

Keywords: fluidized bed, large particles, particle diameter, ANN

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Authors: Abdullah Alnutayfat, Alexander Sutin

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One of the most important types of renewable energy resources is wind energy which can be produced by wind turbines. The blades of the wind turbine are exposed to the pressure of the harsh environment, which causes a significant issue for the wind power industry in terms of the maintenance cost and failure of blades. One of the reliable methods for blade inspection is the vibroacoustic structural health monitoring (SHM) method which examines information obtained from the structural vibrations of the blade. However, all vibroacoustic SHM techniques are based on comparing the structural vibration of intact and damaged structures, which places a practical limit on their use. Methods for nonlinear vibroacoustic SHM are more sensitive to damage and cracking and do not need to be compared to data from the intact structure. This paper presents the Vibro-Acoustic Modulation (VAM) method based on the modulation of high-frequency (probe wave) by low-frequency loads (pump wave) produced by the blade rotation. The blade rotation alternates bending stress due to gravity, leading to crack size variations and variations in the blade resonance frequency. This method can be used with the classical SHM vibration method in which the blade is excited by piezoceramic actuator patches bonded to the blade and receives the vibration response from another piezoceramic sensor. The VAM modification of this method analyzes the spectra of the detected signal and their sideband components. We suggest the VAM model as the simple mechanical oscillator, where the parameters of the oscillator (resonance frequency and damping) are varied due to low-frequency blade rotation. This model uses the blade vibration parameters and crack influence on the blade resonance properties from previous research papers to predict the modulation index (MI).

Keywords: wind turbine blades, damaged detection, vibro-acoustic structural health monitoring, vibro-acoustic modulation

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Authors: Jacob Hatherell, Jason Matthews, Arnaud Marmier

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Anvil Staking is a cold-forming process that is used in the assembly of plain spherical bearings into a rod-end housing. This process ensures that the bearing outer lip conforms to the chamfer in the matching rod end to produce a lightweight mechanical joint with sufficient strength to meet the pushout load requirement of the assembly. Finite Element (FE) analysis is being used extensively to predict the behaviour of metal flow in cold forming processes to support industrial manufacturing and product development. On-going research aims to validate FE models across a wide range of bearing and rod-end geometries by systematically isolating and understanding the uncertainties caused by variations in, material properties, load-dependent friction coefficients and strain rate sensitivity. The improved confidence in these models aims to eliminate the costly and time-consuming process of experimental trials in the introduction of new bearing designs. Previous literature has shown that friction coefficients do not remain constant during cold forming operations, however, the understanding of this phenomenon varies significantly and is rarely implemented in FE models. In this paper, a new approach to evaluate the normal contact pressure versus friction coefficient relationship is outlined using friction calibration charts generated via iterative FE models and ring compression tests. When compared to previous research, this new approach greatly improves the prediction of forming geometry and the forming load during the staking operation. This paper also aims to standardise the FE approach to modelling ring compression test and determining the friction calibration charts.

Keywords: anvil staking, finite element analysis, friction coefficient, spherical plain bearing, ring compression tests

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Authors: E. Karacabey, Ş. G. Özçelik, M. S. Turan, C. Baltacıoğlu, E. Küçüköner

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Changes in textural properties of any food material during processing is significant for further consumer’s evaluation and directly affects their decisions. Thus any food material should be considered in terms of textural properties after any process. In the present study zucchini slices were partially predried to control and reduce the product’s final oil content. A conventional oven was used for partially dehydration of zucchini slices. Following frying was carried in an industrial fryer having temperature controller. This study was based on the effect of this predrying process on textural properties of fried zucchini slices. Texture profile analysis was performed. Hardness, elasticity, chewiness, cohesiveness were studied texture parameters of fried zucchini slices. Temperature and weight loss were monitored parameters of predrying process, whereas, in frying, oil temperature and process time were controlled. Optimization of two successive processes was done by response surface methodology being one of the common used statistical process optimization tools. Models developed for each texture parameters displayed high success to predict their values as a function of studied processes’ conditions. Process optimization was performed according to target values for each property determined for directly fried zucchini slices taking the highest score from sensory evaluation. Results indicated that textural properties of predried and then fried zucchini slices could be controlled by well-established equations. This is thought to be significant for fried stuff related food industry, where controlling of sensorial properties are crucial to lead consumer’s perception and texture related ones are leaders. This project (113R015) has been supported by TUBITAK.

Keywords: optimization, response surface methodology, texture profile analysis, conventional oven, modelling

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Authors: Shu-Lien Chou, Chung-Feng Liu

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Self-health management of chronic illnesses plays an important part in chronic illness treatments. However, various kinds of health information (health education materials) which government or healthcare institutions provide for patients may not achieve the expected outcome. One of the critical reasons affecting patients’ use intention could be patients’ perceived Information overload regarding the health information. This study proposed an extended model of Theory of Planned Behavior, which integrating perceived information overload as another construct to explore patients’ use intention of the health information for self-health management. The independent variables are attitude, subject norm, perceived behavior control and perceived information overload while the dependent variable is behavior intention to use the health information. The cross-sectional study used a structured questionnaire for data collection, focusing on the chronic patients with coronary artery disease (CAD), who are the potential users of the health information, in a medical center in Taiwan. Data were analyzed using descriptive statistics of the basic information distribution of the questionnaire respondents, and the Partial Least Squares (PLS) structural equation model to study the reliability and construct validity for testing our hypotheses. A total of 110 patients were enrolled in this study and 106 valid questionnaires were collected. The PLS analysis result indicates that the patients’ perceived information overload of health information contributes the most critical factor influencing the behavioral intention. Subjective norm and perceived behavioral control of TPB constructs had significant effects on patients’ intentions to use health information also, whereas the attitude construct did not. This study demonstrated a comprehensive framework, which extending perceived information overload into TPB model to predict patients’ behavioral intention of using heath information. We expect that the results of this study will provide useful insights for studying health information from the perspectives of academia, governments, and healthcare providers.

Keywords: chronic patients, health information, information overload, theory of planned behavior

Procedia PDF Downloads 425