Search results for: non-linear modeling

Authors: K. Khouzami, J. Brassinne, C. Branca, E. Van Ruymbeke, B. Nysten, G. D’Angelo

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The development of hybrid organic-inorganic nanocomposites has recently attracted great interest. Typically, polymer silicates represent an emerging class of polymeric nanocomposites that offer superior material properties compared to each compound alone. Among these materials, complexes based on silicate clay and polysaccharides are one of the most promising nanocomposites. The strong electrostatic interaction between chitosan and montmorillonite can induce what is called physical hydrogel, where the coordination bonds or physical crosslinks may associate and dissociate reversibly and in a short time. These mechanisms could be the main origin of the uniqueness of their rheological behavior. However, owing to their structure intrinsically heterogeneous and/or the lack of dissipated energy, they are usually brittle, possess a poor toughness and may not have sufficient mechanical strength. Consequently, the properties of these nanocomposites cannot respond to some requirements of many applications in several fields. To address the issue of weak mechanical properties, covalent chemical crosslink bonds can be introduced to the physical hydrogel. In this way, quite homogeneous dually crosslinked microstructures with high dissipated energy and enhanced mechanical strength can be engineered. In this work, we have prepared a series of chitosan-montmorillonite nanocomposites chemically crosslinked by addition of poly (ethylene glycol) diglycidyl ether. This study aims to provide a better understanding of the mechanical behavior of dually crosslinked chitosan-based nanocomposites by relating it to their microstructures. In these systems, the variety of microstructures is obtained by modifying the number of cross-links. Subsequently, a superior uniqueness of the rheological properties of chemically crosslinked chitosan-montmorillonite nanocomposites is achieved, especially at the highest percentage of clay. Their rheological behaviors depend on the clay/chitosan ratio and the crosslinking. All specimens exhibit a viscous rheological behavior over the frequency range investigated. The flow curves of the nanocomposites show a Newtonian plateau at very low shear rates accompanied by a quite complicated nonlinear decrease with increasing the shear rate. Crosslinking induces a shear thinning behavior revealing the formation of network-like structures. Fitting shear viscosity curves via Ostward-De Waele equation disclosed that crosslinking and clay addition strongly affect the pseudoplasticity of the nanocomposites for shear rates γ ̇>20.

Keywords: chitosan, crossliking, nanocomposites, rheological properties

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Authors: Isaac Owusu Asante, Yushi Jiang, Hailin Tao

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Live streaming marketing, the new electronic commerce element, became an optional marketing channel following the COVID-19 pandemic. Many sellers have leveraged the features presented by live streaming to increase sales. Studies on live streaming have focused on gaming and consumers’ loyalty to brands through live streaming, using interview questionnaires. This study, however, was conducted to measure real-time observable interactions between consumers and sellers. Based on the affordance theory, this study conceptualized constructs representing the interactive features and examined how they drive consumers’ purchase willingness during live streaming sessions using 1238 datasets from Amazon Live, following the manual observation of transaction records. Using structural equation modeling, the ordinary least square regression suggests that live viewers, new followers, live chats, and likes positively affect purchase willingness. The Sobel and Monte Carlo tests show that new followers, live chats, and likes significantly mediate the relationship between live viewers and purchase willingness. The study introduces a new way of measuring interactions in live streaming commerce and proposes a way to manually gather data on consumer behaviors in live streaming platforms when the application programming interface (API) of such platforms does not support data mining algorithms.

Keywords: livestreaming marketing, live chats, live viewers, likes, new followers, purchase willingness

Procedia PDF Downloads 82

Authors: Sarwat Jahan, Asker Rajin Rahman

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Management of offtake of any fluvial river is very sensitive in terms of long-term sustainability where the variation of water flow and sediment transport range are wide enough throughout a hydrological year. The Gorai River is a major distributary of the Ganges River in Bangladesh and is termed as a primary source of fresh water for the South-West part of the country. Every year, significant siltation of the Gorai offtake disconnects it from the Ganges during the dry season. As a result, the socio-economic and environmental condition of the downstream areas has been deteriorating for a few decades. To improve the overall situation of the Gorai offtake and its dependent areas, a study has been conducted by the Institute of Water Modelling, Bangladesh, in 2022. Using the mathematical morphological modeling tool MIKE 21C of DHI Water & Environment, Denmark, simulated results revealed the need for dredging/river training structures for offtake management at the Gorai offtake to ensure significant dry season flow towards the downstream. The dry season flow is found to increase significantly with the proposed river interventions, which also improves the environmental conditions in terms of salinity of the South-West zone of the country. This paper summarizes the primary findings of the analyzed results of the developed mathematical model for improving the existing condition of the Gorai River.

Keywords: Gorai river, mathematical modelling, offtake, siltation, salinity

Procedia PDF Downloads 100

Authors: P. Mounnarath, U. Schmitz, Ch. Zhang

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Fragility analysis is an effective tool for the seismic vulnerability assessment of civil structures in the last several years. The design of the expansion joints according to various bridge design codes is almost inconsistent, and only a few studies have focused on this problem so far. In this study, the influence of the expansion joint clearances between the girder ends and the abutment backwalls on the seismic fragility assessment of continuous integral bridge frames is investigated. The gaps (ranging from 60 mm, 150 mm, 250 mm and 350 mm) are designed by following two different bridge design code specifications, namely, Caltrans and Eurocode 8-2. Five bridge models are analyzed and compared. The first bridge model serves as a reference. This model uses three-dimensional reinforced concrete fiber beam-column elements with simplified supports at both ends of the girder. The other four models also employ reinforced concrete fiber beam-column elements but include the abutment backfill stiffness and four different gap values. The nonlinear time history analysis is performed. The artificial ground motion sets, which have the peak ground accelerations (PGAs) ranging from 0.1 g to 1.0 g with an increment of 0.05 g, are taken as input. The soil-structure interaction and the P-Δ effects are also included in the analysis. The component fragility curves in terms of the curvature ductility demand to the capacity ratio of the piers and the displacement demand to the capacity ratio of the abutment sliding bearings are established and compared. The system fragility curves are then obtained by combining the component fragility curves. Our results show that in the component fragility analysis, the reference bridge model exhibits a severe vulnerability compared to that of other sophisticated bridge models for all damage states. In the system fragility analysis, the reference curves illustrate a smaller damage probability in the earlier PGA ranges for the first three damage states, they then show a higher fragility compared to other curves in the larger PGA levels. In the fourth damage state, the reference curve has the smallest vulnerability. In both the component and the system fragility analysis, the same trend is found that the bridge models with smaller clearances exhibit a smaller fragility compared to that with larger openings. However, the bridge model with a maximum clearance still induces a minimum pounding force effect.

Keywords: expansion joint clearance, fiber beam-column element, fragility assessment, time history analysis

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Authors: Isaac Mugume

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Since the beginning of the 21st century, climate change has been an issue due to the reported rise in global temperature and changes in the frequency as well as severity of extreme weather and climatic events. The changing climate has been attributed to rising concentrations of greenhouse gases, including environmental changes such as ecosystems and land-uses. Climatic projections have been carried out under the auspices of the intergovernmental panel on climate change where a couple of models have been run to inform us about the likelihood of future climates. Since one of the major forcings informing the changing climate is emission of greenhouse gases, different scenarios have been proposed and future climates for different periods presented. The global climate models project different areas to experience different impacts. While regional modeling is being carried out for high impact studies, bias correction is less documented. Yet, the regional climate models suffer bias which introduces uncertainty. This is addressed in this study by bias correcting the regional models. This study uses the Weather Research and Forecasting model under different representative concentration pathways and correcting the products of these models using observed climatic data. This study notes that bias correction (e.g., the running-mean bias correction; the best easy systematic estimator method; the simple linear regression method, nearest neighborhood, weighted mean) improves the climatic projection skill and therefore reduce the uncertainty inherent in the climatic projections.

Keywords: bias correction, climatic projections, numerical models, representative concentration pathways

Procedia PDF Downloads 121

Authors: Anton A. Bykov, Irina O. Glot, Igor N. Shardakov, Alexey P. Shestakov

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This paper presents the results of experimental and theoretical investigations of the mechanisms of crack formation in reinforced concrete beams subjected to quasi-static bending. The boundary-value problem has been formulated in the framework of brittle fracture mechanics and has been solved by using the finite-element method. Numerical simulation of the vibrations of an uncracked beam and a beam with cracks of different size serves to determine the pattern of changes in the spectrum of eigenfrequencies observed during crack evolution. Experiments were performed on the sequential quasistatic four-point bending of the beam leading to the formation of cracks in concrete. At each loading stage, the beam was subjected to an impulse load to induce vibrations. Two stages of cracking were detected. At the first stage the conservative process of deformation is realized. The second stage is an active cracking, which is marked by a sharp change in eingenfrequencies. The boundary of a transition from one stage to another is well registered. The vibration behavior was examined for the beams strengthened by carbon-fiber sheet before loading and at the intermediate stage of loading after the grouting of initial cracks. The obtained results show that the vibrodiagnostic approach is an effective tool for monitoring of cracking and for assessing the quality of measures aimed at strengthening concrete structures.

Keywords: crack formation, experiment, mathematical modeling, reinforced concrete, vibrodiagnostics

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Authors: Sushish Baral, Pratibha Joshi, Yaman Maharjan

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Glaucoma is a chronic eye condition that causes vision loss that is irreversible. Early detection and treatment are critical to prevent vision loss because it can be asymptomatic. For the identification of glaucoma, multiple deep learning algorithms are used. Transformer-based architectures, which use the self-attention mechanism to encode long-range dependencies and acquire extremely expressive representations, have recently become popular. Convolutional architectures, on the other hand, lack knowledge of long-range dependencies in the image due to their intrinsic inductive biases. The aforementioned statements inspire this thesis to look at transformer-based solutions and investigate the viability of adopting transformer-based network designs for glaucoma detection. Using retinal fundus images of the optic nerve head to develop a viable algorithm to assess the severity of glaucoma necessitates a large number of well-curated images. Initially, data is generated by augmenting ocular pictures. After that, the ocular images are pre-processed to make them ready for further processing. The system is trained using pre-processed images, and it classifies the input images as normal or glaucoma based on the features retrieved during training. The Vision Transformer (ViT) architecture is well suited to this situation, as it allows the self-attention mechanism to utilise structural modeling. Extensive experiments are run on the common dataset, and the results are thoroughly validated and visualized.

Keywords: glaucoma, vision transformer, convolutional architectures, retinal fundus images, self-attention, deep learning

Procedia PDF Downloads 192

Authors: Nalinee Sophatsathit

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The objectives of memorabilia of Suan Sunandha are to develop a general knowledge presentation about the historical royal garden through interactive graphic simulation technique and to employ high-functionality context in enhancing interactive user navigation. The approach infers non-intrusive display of relevant history in response to situational context. User’s navigation runs through the virtual reality campus, consisting of new and restored buildings. A flash back presentation of information pertaining to the history in the form of photos, paintings, and textual descriptions are displayed along each passing-by building. To keep the presentation lively, graphical simulation is created in a serendipity game play so that the user can both learn and enjoy the educational tour. The benefits of this human-computer interaction development are two folds. First, lively presentation technique and situational context modeling are developed that entail a usable paradigm of knowledge and information presentation combinations. Second, cost effective training and promotion for both internal personnel and public visitors to learn and keep informed of this historical royal garden can be furnished without the need for a dedicated public relations service. Future improvement on graphic simulation and ability based display can extend this work to be more realistic, user-friendly, and informative for all.

Keywords: interactive user navigation, high-functionality context, situational context, human-computer interaction

Procedia PDF Downloads 359

Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

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Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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Authors: Jalila Jbilou, Salaheddine El Adlouni, Pierre Gosselin

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Background: West Nile virus is increasingly an important public health issue in North America. In Canada, WVN was officially reported in Toronto and Montréal for the first time in 2001. During the last decade, several WNV events have been reported in several Canadian provinces. The main objective of the present study is to update the frequency of the climate conditions favorable to WNV outbreaks in Canada. Method: Statistical frequency analysis has been used to estimate the return period for climate conditions associated with WNV outbreaks for the 1961–2050 period. The best fit is selected through the Akaike Information Criterion, and the parameters are estimated using the maximum likelihood approach. Results: Results show that the climate conditions related to the 2002 event, for Montreal and Toronto, are becoming more frequent. For Saskatoon, the highest DD20 events recorded for the last few decades were observed in 2003 and 2007. The estimated return periods are 30 years and 70 years, respectively. Conclusion: The emergence of WNV was related to extremely high DD values in the summer. However, some exceptions may be related to several factors such as virus persistence, vector migration, and also improved diagnosis and reporting levels. It is clear that such climate conditions have become much more common in the last decade and will likely continue to do so over future decades.

Keywords: West Nile virus, climate, North America, statistical frequency analysis, risk estimation, public health, modeling, scenario, temperature, precipitation

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Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

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

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

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

Procedia PDF Downloads 375

Authors: Mahdi Sadeghian, Somaye Sadeghian, Reza Dinarvand

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This paper is going to discuss two issues encountered in using PLAXIS. Both issues were monitored during application of PLAXIS to estimate the excavation-induced displacement. Column Soil Mixing (CSM) was applied to stabilise the excavation. It was understood that the estimated excavation induced deformation at the top of the CSM blocks highly depends on the material type defining pavement material adjacent to the CSM blocks. Cohesive material for pavement will result in the unrealistic connection between pavement and CSM even by defining an interface element. To find the most realistic approach, the interface defined in three different manners (1) no interface elements were applied (2) a non-cohesive soil layer was defined between pavement and CSM block to represent the friction between these materials (3) built-in interface elements in PLAXIS was used to define the boundary between the pavement and the CSM block. The result showed that the option 2 would result in more realistic results. The second issue was in the modelling of the contact line between the CSM block and an inclined layer underneath. The analysis result showed that the excavation-induced deformation highly depends on how the PLAXIS user defines the contact area. It was understood that if the contact area had defined as a point in which CSM block had intersected the layer underneath the estimated lateral displacement of CSM block would be unrealistically lower than the model in which the contact area was defined as a line.

Keywords: PLAXIS, FEM, CSM, Excavation-Induced Deformation

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Authors: A. Ghanami, M.Heydari

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Heat pipe is simple heat transfer device which combines the conduction and phase change phenomena to control the heat transfer without any need for external power source. At hot surface of heat pipe, the liquid phase absorbs heat and changes to vapor phase. The vapor phase flows to condenser region and with the loss of heat changes to liquid phase. Due to gravitational force the liquid phase flows to evaporator section. In HVAC systems the working fluid is chosen based on the operating temperature. The heat pipe has significant capability to reduce the humidity in HVAC systems. Each HVAC system which uses heater, humidifier or dryer is a suitable nominate for the utilization of heat pipes. Generally heat pipes have three main sections: condenser, adiabatic region and evaporator. Performance investigation and optimization of heat pipes operation in order to increase their efficiency is crucial. In present article, a parametric study is performed to improve the heat pipe performance. Therefore, the heat capacity of heat pipe with respect to geometrical and confining parameters is investigated. For the better observation of heat pipe operation in HVAC systems, a CFD simulation in Eulerian- Eulerian multiphase approach is also performed. The results show that heat pipe heat transfer capacity is higher for water as working fluid with the operating temperature of 340 K. It is also showed that the vertical orientation of heat pipe enhances it’s heat transfer capacity.used in the abstract.

Keywords: Heat pipe, HVAC system, Grooved Heat pipe, Heat pipe limits.

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Authors: Mehdi Khakzand, Flora Fakourian

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It has been seen in recent years that architects are using virtual modeling to help them visualize their projects. Research has indicated that virtual media, particularly virtual reality, enhances architects' comprehension of design and spatial perception. Creating a communal experience for active learning is an essential component of the design process in architecture pedagogy. It has been particularly challenging to replicate design principles as a critical teaching function, and this is a complex issue that demands comprehension. Nonetheless, the usage of simulation should be studied and limited as appropriate. In conjunction with extensive technology, 3D geometric illustration can bridge the gap between the real and virtual worlds. This research intends to deliver a pedagogical experience in the architecture basics course to improve the architectural design process utilizing virtual reality tools. This tool seeks to tackle current challenges in current ways of architectural illustration by offering building geometry illustration, building information (data from the building information model), and simulation results. These tools were tested over three days in a design workshop with 12 architectural students. This article provided an architectural VR-based course and explored its application in boosting students' active experiences. According to the research, this technology can improve students' cognitive skills from challenging simulations by boosting visual understanding.

Keywords: active experience, architecture pedagogy, virtual reality, spatial perception

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Authors: Sebahat Seker

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Since there is a parallel connection between drinking water quality and public health, studies on drinking and domestic water are of vital importance. Ardahan Province is one of the provinces located in the Northeast Anatolian Region, where animal husbandry and agriculture are carried out economically. City mains water uses underground spring water as a source and is chlorinated and given to the city center by gravity. However, mains water cannot be used outside the central district of the city, and the majority of the people meet their drinking and utility water needs from the wells they have opened individually. The water element, which is vital for all living things, is the most important substance that sustains life for humans. Under normal conditions, a healthy person consumes approximately 1.8-2 liters of water. The quality and use of potable water is one of the most important issues in terms of health. The quality parameters of drinking and utility water have been revealed by the scientific world. Scientific studies on drinking water quality in the world and its impact on public health are among the most popular topics. Although our country is surrounded by water on three sides, potable water resources are very few. In the Eastern Anatolia Region, it is difficult for the public to access drinking and utility water due to the difficult conditions both climatically and geographically. In this study, samples taken from drinking and utility water at certain intervals from the stations determined, and water quality parameters will be determined. The fact that such a study has not been carried out in the region before and the knowledge of the local people about water quality is very important in terms of its original and widespread effect.

Keywords: water quality, modelling, evaluation, northeastern anatolia

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Authors: Sahar Heidary, Ramin Ghasemi Shayan

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The overall goal Monte Carlo N-atom radioactivity transference PC program (MCNP4C) was done for the regeneration of x-ray groups in diagnostic radiology and mammography. The electrons were transported till they slow down and stopover in the target. Both bremsstrahlung and characteristic x-ray creation were measured in this study. In this issue, the x-ray spectra forecast by several computational models recycled in the diagnostic radiology and mammography energy kind have been calculated by appraisal with dignified spectra and their outcome on the scheming of absorbed dose and effective dose (ED) told to the adult ORNL hermaphroditic phantom quantified. This comprises practical models (TASMIP and MASMIP), semi-practical models (X-rayb&m, X-raytbc, XCOMP, IPEM, Tucker et al., and Blough et al.), and Monte Carlo modeling (EGS4, ITS3.0, and MCNP4C). Images got consuming synchrotron radiation (SR) and both screen-film and the CR system were related with images of the similar trials attained with digital mammography equipment. In sight of the worthy feature of the effects gained, the CR system was used in two mammographic inspections with SR. For separately mammography unit, the capability acquiesced bilateral mediolateral oblique (MLO) and craniocaudal(CC) mammograms attained in a woman with fatty breasts and a woman with dense breasts. Referees planned the common groups and definite absences that managed to a choice to miscarry the part that formed the scientific imaginings.

Keywords: mammography, monte carlo, effective dose, radiology

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Authors: Shin-Hyung Cho, Dong-Kyu Kim, Seung-Young Kho

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Truck-involved crashes have higher crash severity than non-truck-involved crashes. There have been many studies about the frequency of crashes and the development of severity models, but those studies only analyzed the relationship between observed variables. To identify why more people are injured or killed when trucks are involved in the crash, we must examine to quantify the complex causal relationship between severity of the crash and risk factors by adopting the latent factors of crashes. The aim of this study was to develop a structural equation or model based on truck-involved and non-truck-involved crashes, including five latent variables, i.e. a crash factor, environmental factor, road factor, driver’s factor, and severity factor. To clarify the unique characteristics of truck-involved crashes compared to non-truck-involved crashes, a confirmatory analysis method was used. To develop the model, we extracted crash data from 10,083 crashes on Korean freeways from 2008 through 2014. The results showed that the most significant variable affecting the severity of a crash is the crash factor, which can be expressed by the location, cause, and type of the crash. For non-truck-involved crashes, the crash and environment factors increase severity of the crash; conversely, the road and driver factors tend to reduce severity of the crash. For truck-involved crashes, the driver factor has a significant effect on severity of the crash although its effect is slightly less than the crash factor. The multiple group analysis employed to analyze the differences between the heterogeneous groups of drivers.

Keywords: crash severity, structural structural equation modeling (SEM), truck-involved crashes, multiple group analysis, crash on freeway

Procedia PDF Downloads 384

Authors: Ali Jahanfar, Brajesh Dubey, Bahram Gharabaghi, Saber Bayat Movahed

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Ever increasing population growth of major urban centers and environmental challenges in siting new landfills have resulted in a growing trend in design of mega-landfills some with extraordinary heights and dangerously steep slopes. Landfill failure mobility risk analysis is one of the most uncertain types of dynamic rheology models due to very large inherent variabilities in the heterogeneous solid waste material shear strength properties. The waste flow of three historic dumpsite and two landfill failures were back-analyzed using run-out modeling with DAN-W model. The travel distances of the waste flow during landfill failures were calculated approach by taking into account variability in material shear strength properties. The probability distribution function for shear strength properties of the waste material were grouped into four major classed based on waste material compaction (landfills versus dumpsites) and composition (high versus low quantity) of high shear strength waste materials such as wood, metal, plastic, paper and cardboard in the waste. This paper presents a probabilistic method for estimation of the spatial extent of waste avalanches, after a potential landfill failure, to create maps of vulnerability scores to inform property owners and residents of the level of the risk.

Keywords: landfill failure, waste flow, Voellmy rheology, friction coefficient, waste compaction and type

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Authors: Paul Buchana, Patrick E. Mc Sharry

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In 2017 there will be about 38 wind farms in the North Sea belonging to 5 different countries. The North Sea is ideal for offshore wind power generation and is thus attractive to offshore wind energy developers and investors. With concerns about the potential for offshore wind turbines to sustain substantial damage as a result of extreme weather conditions, particularly windstorms, this poses a unique challenge to insurers and reinsurers as to adequately quantify the risk and offer appropriate insurance cover for these assets. The need to manage this risk also concerns regulators, who provide the oversight needed to ensure that if a windstorm or a series of storms occur in this area over a one-year time frame, the insurers of these assets in the EU remain solvent even after meeting consequent damage costs. In this paper, using available European windstorm data for the past 33 years and actual wind farm locations together with information pertaining to each of the wind farms (number of turbines, total capacity and financial value), we present a Monte Carlo simulation approach to assess the number of turbines that would be buckled in each of the wind farms using maximum wind speeds reaching each of them. These wind speeds are drawn from historical windstorm data. From the number of turbines buckled, associated financial loss and output capacity can be deduced. The results presented in this paper are targeted towards offshore wind energy developers, insurance and reinsurance companies and regulators.

Keywords: catastrophe modeling, North Sea wind farms, offshore wind power, risk analysis

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Authors: Yasaman Sanayei, Alireza Bahiraie

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This paper presents a systematic methodology based on the application of artificial neural networks for sequencing batch reactor (SBR). The SBR is a fill-and-draw biological wastewater technology, which is specially suited for nutrient removal. Employing reactive dye by Sphingomonas paucimobilis bacteria at sequence batch reactor is a novel approach of dye removal. The influent COD, MLVSS, and reaction time were selected as the process inputs and the effluent COD and BOD as the process outputs. The best possible result for the discrete pole parameter was a= 0.44. In orderto adjust the parameters of ANN, the Levenberg-Marquardt (LM) algorithm was employed. The results predicted by the model were compared to the experimental data and showed a high correlation with R2> 0.99 and a low mean absolute error (MAE). The results from this study reveal that the developed model is accurate and efficacious in predicting COD and BOD parameters of the dye-containing wastewater treated by SBR. The proposed modeling approach can be applied to other industrial wastewater treatment systems to predict effluent characteristics. Note that SBR are normally operated with constant predefined duration of the stages, thus, resulting in low efficient operation. Data obtained from the on-line electronic sensors installed in the SBR and from the control quality laboratory analysis have been used to develop the optimal architecture of two different ANN. The results have shown that the developed models can be used as efficient and cost-effective predictive tools for the system analysed.

Keywords: artificial neural network, COD removal, SBR, Sphingomonas paucimobilis

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Authors: Assem M. F. Sallam, Ah. El-S. Makled

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In this paper, there is an implementation, verification, and graphical demonstration of a software application, which can be used swiftly over different preliminary orbit determination methods. A passive orbit determination method is used in this study to determine the location of a satellite or a flying body. It is named a passive orbit determination because it depends on observation without the use of any aids (radio and laser) installed on satellite. In order to understand how these methods work and how their output is accurate when compared with available verification data, the built models help in knowing the different inputs used with each method. Output from the different orbit determination methods (Gibbs, Lambert, and Gauss) will be compared with each other and verified by the data obtained from Satellite Tool Kit (STK) application. A modified model including all of the orbit determination methods using the same input will be introduced to investigate different models output (orbital parameters) for the same input (azimuth, elevation, and time). Simulation software is implemented using MATLAB. A Graphical User Interface (GUI) application named OrDet is produced using the GUI of MATLAB. It includes all the available used inputs and it outputs the current Classical Orbital Elements (COE) of satellite under observation. Produced COE are then used to propagate for a complete revolution and plotted on a 3-D view. Modified model which uses an adapter to allow same input parameters, passes these parameters to the preliminary orbit determination methods under study. Result from all orbit determination methods yield exactly the same COE output, which shows the equality of concept in determination of satellite’s location, but with different numerical methods.

Keywords: orbit determination, STK, Matlab-GUI, satellite tracking

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Authors: Salah Al-Enezi, Rashed Al-Zufairi, Naseer Ahmad

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A Thermo-mechanical technique was developed to determine softening point temperature/glass transition temperature (Tg) of polystyrene exposed to high pressures. The design utilizes the ability of carbon dioxide to lower the glass transition temperature of polymers and acts as plasticizer. In this apparatus, the sorption of carbon dioxide to induce softening of polymers as a function of temperature/pressure is performed and the extent of softening is measured in three-point-flexural-bending mode. The polymer strip was placed in the cell in contact with the linear variable differential transformer (LVDT). CO₂ was pumped into the cell from a supply cylinder to reach high pressure. The results clearly showed that full softening point of the samples, accompanied by a large deformation on the polymer strip. The deflection curves are initially relatively flat and then undergo a dramatic increase as the temperature is elevated. It was found that increasing the pressure of CO₂ causes the temperature curves to shift from higher to lower by increment of about 45 K, over the pressure range of 0-120 bars. The obtained experimental Tg values were validated with the values reported in the literature. Finally, it is concluded that the defection model fits consistently to the generated experimental results, which attempts to describe in more detail how the central deflection of a thin polymer strip affected by the CO₂ diffusions in the polymeric samples.

Keywords: softening, high-pressure, polystyrene, CO₂ diffusions

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Authors: Mustafa Alhamdi

Abstract:

Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

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Authors: Khan R. Rahaman, Quazi K. Hassan

Abstract:

Global changes in climate, environment, economies, populations, governments, institutions, and cultures converge in localities. Changes at a local scale, in turn, contribute to global changes as well as being affected by them. Our hypothesis is built on a consideration that temperature does vary at local level (i.e., termed as local warming) in comparison to the predicted models at the regional and/or global scale. To date, the bulk of the research relating local places to global climate change has been top-down, from the global toward the local, concentrating on methods of impact analysis that use as a starting point climate change scenarios derived from global models, even though these have little regional or local specificity. Thus, our focus is to understand such trends over the southern Alberta, which will enable decision makers, scientists, researcher community, and local people to adapt their policies based on local level temperature variations and to act accordingly. Specific objectives in this study are: (i) to understand the local warming (temperature in particular) trend in context of temperature normal during the period 1961-2010 at point locations using meteorological data; (ii) to validate the data by using specific yearly data, and (iii) to delineate the spatial extent of the local warming trends and understanding influential factors to adopt situation by local governments. Existing data has brought the evidence of such changes and future research emphasis will be given to validate this hypothesis based on remotely sensed data (i.e. MODIS product by NASA).

Keywords: local warming, climate change, urban area, Alberta, Canada

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Authors: Chiraz Ben Jabeur, Hassene Seddik

Abstract:

In this paper, a full approach of modeling and control of a four-rotor unmanned air vehicle (UAV), known as quad-rotor aircraft, is presented. In fact, a PD and a PD optimized Neural Networks Approaches (PD-NN) are developed to be applied to control a quad-rotor. The goal of this work is to concept a smart self-tuning PD controller based on neural networks able to supervise the quad-rotor for an optimized behavior while tracking the desired trajectory. Many challenges could arise if the quad-rotor is navigating in hostile environments presenting irregular disturbances in the form of wind added to the model on each axis. Thus, the quad-rotor is subject to three-dimensional unknown static/varying wind disturbances. The quad-rotor has to quickly perform tasks while ensuring stability and accuracy and must behave rapidly with regard to decision-making facing disturbances. This technique offers some advantages over conventional control methods such as PD controller. Simulation results are obtained with the use of Matlab/Simulink environment and are founded on a comparative study between PD and PD-NN controllers based on wind disturbances. These later are applied with several degrees of strength to test the quad-rotor behavior. These simulation results are satisfactory and have demonstrated the effectiveness of the proposed PD-NN approach. In fact, this controller has relatively smaller errors than the PD controller and has a better capability to reject disturbances. In addition, it has proven to be highly robust and efficient, facing turbulences in the form of wind disturbances.

Keywords: hostile environment, PD and PD-NN controllers, quad-rotor control, robustness against disturbance

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Authors: Ziya Uddin

Abstract:

This paper deals with the theoretical and numerical investigation of magneto-hydrodynamic boundary layer flow of a nano fluid past a wedge shaped wick in heat pipe used for the cooling of electronic components and different type of machines. To incorporate the effect of nanoparticle diameter, concentration of nanoparticles in the pure fluid, nano thermal layer formed around the nanoparticle and Brownian motion of nano particles etc., appropriate models are used for the effective thermal and physical properties of nano fluids. To model the rotation of nano particles inside the base fluid, microfluidics theory is used. In this investigation ethylene glycol (EG) based nanofluids, are taken into account. The non-linear equations governing the flow and heat transfer are solved by using a very effective particle swarm optimization technique along with Runge-Kutta method. The values of heat transfer coefficient are found for different parameters involved in the formulation viz. nanoparticle concentration, nanoparticle size, magnetic field and wedge angle etc. It is found that the wedge angle, presence of magnetic field, nanoparticle size and nanoparticle concentration etc. have prominent effects on fluid flow and heat transfer characteristics for the considered configuration.

Keywords: nanofluids, wedge shaped wick, heat pipe, numerical modeling, particle swarm optimization, nanofluid applications, Heat transfer

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Authors: Yun Chol Kang, Myong Nam Kong, Nam Chol Yu, Jin Sim Kim, Un Yong Paek, Song Ho Kim

Abstract:

This study focuses on the calculating approach of the thermal conductivity of 8 mol% yttria-stabilized zirconia (8YSZ) in different relative humidity corresponding to low water contents. When water content in 8YSZ is low, water droplets can accumulate in the neck regions. We assume that spherical water droplets are randomly located in the neck regions formed by grains and surrounded by the pores. Based on this, a new hypothetical pore constituted by air and water is proposed using the microstructural modeling. We consider 8YSZ is a two-phase material constituted by the solid region and the hypothetical pore region where the water droplets are penetrated in the pores, randomly. The results showed that the thermal conductivity of the hypothetical pore is calculated using the parallel resistance for low water contents, and the effective thermal conductivity of 8YSZ material constituted by solid and hypothetical pore in different relative humidities using EMPT. When the numbers of water layers on the surface of 8YSZ are less than 1.5, the proposed approach gives a good interpretation of the experimental results. When the theoretical value of the number of water layers on 8YSZ surface is 1, the water content is not enough to cover the internal solid surface completely. The proposed approach gives a better interpretation of the experimental results in different relative humidities that numbers of water layers on the surface of 8YSZ are less than 1.5.

Keywords: 8YSZ, microstructure, thermal conductivity, relative humidity

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

Abstract:

In multi-echelon supply chain networks, optimal supplier selection significantly depends on the accuracy of suppliers’ performance prediction. Different methods of multi criteria decision making such as ANN, GA, Fuzzy, AHP, etc have been previously used to predict the supplier performance but the “black-box” characteristic of these methods is yet a major concern to be resolved. Therefore, the primary objective in this paper is to implement an artificial intelligence-based gene expression programming (GEP) model to compare the prediction accuracy with that of ANN. A full factorial design with %95 confidence interval is initially applied to determine the appropriate set of criteria for supplier performance evaluation. A test-train approach is then utilized for the ANN and GEP exclusively. The training results are used to find the optimal network architecture and the testing data will determine the prediction accuracy of each method based on measures of root mean square error (RMSE) and correlation coefficient (R2). The results of a case study conducted in Supplying Automotive Parts Co. (SAPCO) with more than 100 local and foreign supply chain members revealed that, in comparison with ANN, gene expression programming has a significant preference in predicting supplier performance by referring to the respective RMSE and R-squared values. Moreover, using GEP, a mathematical function was also derived to solve the issue of ANN black-box structure in modeling the performance prediction.

Keywords: Supplier Performance Prediction, ANN, GEP, Automotive, SAPCO

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Authors: Yin-Yee Leong, Jack C. Yue, Hsin-Chung Wang

Abstract:

Human longevity has been experiencing its largest increase since the end of World War II, and modeling the mortality rates is therefore often the focus of many studies. Among all mortality models, the Lee–Carter model is the most popular approach since it is fairly easy to use and has good accuracy in predicting mortality rates (e.g., for Japan and the USA). However, empirical studies from several countries have shown that the age parameters of the Lee–Carter model are not constant in time. Many modifications of the Lee–Carter model have been proposed to deal with this problem, including adding an extra cohort effect and adding another period effect. In this study, we propose a spatial modification and use clusters to explain why the age parameters of the Lee–Carter model are not constant. In spatial analysis, clusters are areas with unusually high or low mortality rates than their neighbors, where the “location” of mortality rates is measured by age and time, that is, a 2-dimensional coordinate. We use a popular cluster detection method—Spatial scan statistics, a local statistical test based on the likelihood ratio test to evaluate where there are locations with mortality rates that cannot be described well by the Lee–Carter model. We first use computer simulation to demonstrate that the cluster effect is a possible source causing the problem of the age parameters not being constant. Next, we show that adding the cluster effect can solve the non-constant problem. We also apply the proposed approach to mortality data from Japan, France, the USA, and Taiwan. The empirical results show that our approach has better-fitting results and smaller mean absolute percentage errors than the Lee–Carter model.

Keywords: mortality improvement, Lee–Carter model, spatial statistics, cluster detection

Procedia PDF Downloads 172