Search results for: large Eddy simulation

Authors: A. Ascenso, C. Silveira, B. Augusto, S. Rafael, S. Coelho, J. Ferreira, A. Monteiro, P. Roebeling, A. I. Miranda

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Innovative nature-based solutions (NBSs) can provide answers to the challenges that urban areas are currently facing due to urban densification and extreme weather conditions. The effects of NBSs are recognized and include improved quality of life, mental and physical health and improvement of air quality, among others. Part of the work developed in the scope of the UNaLab project, which aims to guide cities in developing and implementing their own co-creative NBSs, intends to assess the impacts of NBSs on air quality, using Eindhoven city as a case study. The state-of-the-art online air quality modelling system WRF-CHEM was applied to simulate meteorological and concentration fields over the study area with a spatial resolution of 1 km2 for the year 2015. The baseline simulation (without NBSs) was validated by comparing the model results with monitored data retrieved from the Eindhoven air quality database, showing an adequate model performance. In addition, land use changes were applied in a set of simulations to assess the effects of different types of NBSs. Finally, these simulations were compared with the baseline scenario and the impacts of the NBSs were assessed. Reductions on pollutant concentrations, namely for NOx and PM, were found after the application of the NBSs in the Eindhoven study area. The present work is particularly important to support public planners and decision makers in understanding the effects of their actions and planning more sustainable cities for the future.

Keywords: air quality, modelling approach, nature based solutions, urban area

Procedia PDF Downloads 219

Authors: Yun-Tao Zhang, Jong-Yeop Bae, Whoi-Yul Kim

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Background modeling and subtraction in video analysis has been widely proved to be an effective method for moving objects detection in many computer vision applications. Over the past years, a large number of approaches have been developed to tackle different types of challenges in this field. However, the dynamic background and illumination variations are two of the most frequently occurring issues in the practical situation. This paper presents a new two-layer model based on codebook algorithm incorporated with local binary pattern (LBP) texture measure, targeted for handling dynamic background and illumination variation problems. More specifically, the first layer is designed by block-based codebook combining with LBP histogram and mean values of RGB color channels. Because of the invariance of the LBP features with respect to monotonic gray-scale changes, this layer can produce block-wise detection results with considerable tolerance of illumination variations. The pixel-based codebook is employed to reinforce the precision from the outputs of the first layer which is to eliminate false positives further. As a result, the proposed approach can greatly promote the accuracy under the circumstances of dynamic background and illumination changes. Experimental results on several popular background subtraction datasets demonstrate a very competitive performance compared to previous models.

Keywords: background subtraction, codebook model, local binary pattern, dynamic background, illumination change

Procedia PDF Downloads 199

Authors: Ke He, Wumaier Parezhati, Haruka Yamashita

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Recently, online marketplaces in the e-commerce industry, such as Rakuten and Alibaba, have become some of the most popular online marketplaces in Asia. In these shopping websites, consumers can select purchase products from a large number of stores. Additionally, consumers of the e-commerce site have to register their name, age, gender, and other information in advance, to access their registered account. Therefore, establishing a method for analyzing consumer preferences from both the store and the product side is required. This study uses the Doc2Vec method, which has been studied in the field of natural language processing. Doc2Vec has been used in many cases to analyze the extraction of semantic relationships between documents (represented as consumers) and words (represented as products) in the field of document classification. This concept is applicable to represent the relationship between users and items; however, the problem is that one more factor (i.e., shops) needs to be considered in Doc2Vec. More precisely, a method for analyzing the relationship between consumers, stores, and products is required. The purpose of our study is to combine the analysis of the Doc2vec model for users and shops, and for users and items in the same feature space. This method enables the calculation of similar shops and items for each user. In this study, we derive the real data analysis accumulated in the online marketplace and demonstrate the efficiency of the proposal.

Keywords: Doc2Vec, online marketplace, marketing, recommendation systems

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Authors: Zhiwen Yang, Jingyuan Li, Zhenkai Xie, Jian Ling, Jiguang Wang, Mengliang Li

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To reflect the effects of different emission control strategies, such as retrofitting after-treatment system and replacing with natural gas-fueled vehicles, on particle number (PN), particle mass (PM) and nitrogen oxides (NOx) emissions emitted by urban bus, a portable emission measurement system (PEMS) was employed herein to conduct real-world driving emission measurements on a diesel oxidation catalytic converter (DOC) and catalyzed diesel particulate filter (CDPF) retrofitting China IV diesel bus, a China IV diesel bus, and a China V natural gas bus. The results show that both tested diesel buses possess markedly advantages in NOx emission control when compared to the lean-burn natural gas bus equipped without any NOx after-treatment system. As to PN and PM, only the DOC+CDPF retrofitting diesel bus exhibits enormous benefits on emission control relate to the natural gas bus, especially the normal diesel bus. Meanwhile, the differences in PM and PN emissions between retrofitted and normal diesel buses generally increase with the increase in vehicle-specific power (VSP). Furthermore, the differences in PM emissions, especially those in the higher VSP ranges, are more significant than those in PN. In addition, the maximum peak PN particle size (32 nm) of the retrofitted diesel bus was significantly lower than that of the normal diesel bus (100 nm). These phenomena indicate that the CDPF retrofitting can effectively reduce diesel bus exhaust particle emissions, especially those with large particle sizes.

Keywords: CDPF, diesel, natural gas, real-world emissions

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Authors: Yooseo Pang, Jongwoong Choi, Yong Cho, Yongchae Jeong

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Climate crisis, such as environmental problems related to energy supply, is getting emerged issues, so the use of renewable energy is essentially required to solve these problems, which are mainly managed by the Paris Agreement, the international treaty on climate change. The government of the Republic of Korea announced that the key long-term goal for a low-carbon strategy is “Carbon neutrality by 2050”. It is focused on the role of the internet data centers (IDC) in which large amounts of data, such as artificial intelligence (AI) and big data as an impact of the 4th industrial revolution, are managed. The demand for the cooling system market for IDC was about 9 billion US dollars in 2020, and 15.6% growth a year is expected in Korea. It is important to control the temperature in IDC with an efficient air conditioning system, so hydrothermal energy is one of the best options for saving energy in the cooling system. In order to save energy and optimize the operating conditions, it has been considered to apply ‘the dam deep water air conditioning system. Deep water at a specific level from the dam can supply constant water temperature year-round. It will be tested & analyzed the amount of energy saving with a pilot plant that has 100RT cooling capacity. Also, a target of this project is 1.2 PUE (Power Usage Effectiveness) which is the key parameter to check the efficiency of the cooling system.

Keywords: hydrothermal energy, HVAC, internet data center, free-cooling

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Authors: Kim Byung-Kon, Kim Young-Jin

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As project opportunities for the Architecture, Engineering and Construction (AEC) industry have grown more complex and larger, the utilization of BIM (Building Information Modeling) technologies for 3D design and simulation practices has been increasing significantly; the typical applications of the BIM technologies include clash detection and design alternative based on 3D planning, which have been expanded over to the technology of construction management in the AEC industry for virtual design and construction. As for now, commercial BIM software has been operated under a single-user environment, which is why initial costs for its introduction are very high. Cloud computing, one of the most promising next-generation Internet technologies, enables simple Internet devices to use services and resources provided with BIM software. Recently in Korea, studies to link between BIM and cloud computing technologies have been directed toward saving costs to build BIM-related infrastructure, and providing various BIM services for small- and medium-sized enterprises (SMEs). This study addressed how to develop the usage metering functions of BIM software under cloud computing architecture in order to archive and use BIM data and create an optimal revenue structure so that the BIM services may grow spontaneously, considering a demand for cloud resources. To this end, the author surveyed relevant cases, and then analyzed needs and requirements from AEC industry. Based on the results & findings of the foregoing survey & analysis, the author proposed herein how to optimally develop the usage metering functions of cloud BIM software.

Keywords: construction IT, BIM (Building Information Modeling), cloud computing, BIM-based cloud computing, 3D design, cloud BIM

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Authors: Ali Shareef, Aliha Shareef, Yifeng Zhu

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Energy minimization is of great importance in wireless sensor networks in extending the battery lifetime. One of the key activities of nodes in a WSN is communication and the routing of their data to a centralized base-station or sink. Routing using the shortest path to the sink is not the best solution since it will cause nodes along this path to fail prematurely. We propose a cross-layer energy efficient routing protocol Optrix that utilizes a convex formulation to maximize the lifetime of the network as a whole. We further propose, Optrix-BW, a novel convex formulation with bandwidth constraint that allows the channel conditions to be accounted for in routing. By considering this key channel parameter we demonstrate that Optrix-BW is capable of congestion control. Optrix is implemented in TinyOS, and we demonstrate that a relatively large topology of 40 nodes can converge to within 91% of the optimal routing solution. We describe the pitfalls and issues related with utilizing a continuous form technique such as convex optimization with discrete packet based communication systems as found in WSNs. We propose a routing controller mechanism that allows for this transformation. We compare Optrix against the Collection Tree Protocol (CTP) and we found that Optrix performs better in terms of convergence to an optimal routing solution, for load balancing and network lifetime maximization than CTP.

Keywords: wireless sensor network, Energy Efficient Routing

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Authors: Rong-Terng Juang

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Autonomous vehicles offer the possibility of significant benefits to social welfare. However, fully automated cars might not be going to happen in the near further. To speed the adoption of the self-driving technologies, many governments worldwide are passing laws requiring data recorders for the testing of autonomous vehicles. Currently, the self-driving vehicle, (e.g., shuttle bus) has to be monitored from a remote control center. When an autonomous vehicle encounters an unexpected driving environment, such as road construction or an obstruction, it should request assistance from a remote operator. Nevertheless, large amounts of data, including images, radar and lidar data, etc., have to be transmitted from the vehicle to the remote center. Therefore, this paper proposes a data compression method of in-vehicle networks for remote monitoring of autonomous vehicles. Firstly, the time-series data are rearranged into a multi-dimensional signal space. Upon the arrival, for controller area networks (CAN), the new data are mapped onto a time-data two-dimensional space associated with the specific CAN identity. Secondly, the data are sampled based on differential sampling. Finally, the whole set of data are encoded using existing algorithms such as Huffman, arithmetic and codebook encoding methods. To evaluate system performance, the proposed method was deployed on an in-house built autonomous vehicle. The testing results show that the amount of data can be reduced as much as 1/7 compared to the raw data.

Keywords: autonomous vehicle, data compression, remote monitoring, controller area networks (CAN), Lidar

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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Authors: Bowei Yuan, Shi Li, Liuyang Song, Huaqing Wang, Lingli Cui

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Convolutional neural networks (CNN) have received extensive attention in the field of fault diagnosis. Many fault diagnosis methods use CNN for fault type identification. However, when the amount of raw data collected by sensors is massive, the neural network needs to perform a time-consuming classification task. In this paper, a mechanical fault diagnosis method based on vibration signal down-sampling and the improved one-dimensional convolutional neural network is proposed. Through the robust principal component analysis, the low-rank feature matrix of a large amount of raw data can be separated, and then down-sampling is realized to reduce the subsequent calculation amount. In the improved one-dimensional CNN, a smaller convolution kernel is used to reduce the number of parameters and computational complexity, and regularization is introduced before the fully connected layer to prevent overfitting. In addition, the multi-connected layers can better generalize classification results without cumbersome parameter adjustments. The effectiveness of the method is verified by monitoring the signal of the centrifugal pump test bench, and the average test accuracy is above 98%. When compared with the traditional deep belief network (DBN) and support vector machine (SVM) methods, this method has better performance.

Keywords: fault diagnosis, vibration signal down-sampling, 1D-CNN

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Authors: Despina Vamvuka, Despina Pentari

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Forestry wastes are readily available in large quantities around the world. Based on European Green Deal for the deployment of renewable and decarbonized energy by 2050, as well as global energy crisis, energy recovery from such wastes reducing greenhouse gas emissions is very attractive. Gasification has superior environmental performance to combustion, producing a clean fuel gas utilized in internal combustion engines, gas turbines, solid oxide fuel cells, or for synthesis of liquid bio-fuels and value-added chemicals. In this work, pine needles, which are abundantly found in Mediterranean countries, were gasified by either steam or carbon dioxide via a two-step process to improve reactivity and eliminate tar, employing a fixed bed unit and a thermal analysis system. Solid, liquid and gaseous products from the whole process were characterized and their energy potential was determined. Thermal behaviour, reactivity, conversion and energy recovery were examined. The gasification process took place above 650°C. At 950°C conversion and energy recovery were 77% dry and 2 under a flow of steam and 85% dry and 2.9 under a flow of carbon dioxide, respectively. Organic matter was almost completely converted to syngas, the yield of which varied between 89% and 99%. The higher heating values of biochar, bio-oil and pyrolysis gas were 27.8 MJ/kg, 33.5 MJ/kg and 13.6 MJ/m3. Upon steam or carbon dioxide gasification, the higher heating value of syngas produced was 11.5 MJ/m3 and 12.7 MJ/m3, respectively.

Keywords: gasification, biomass, steam, carbon dioxide

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Authors: Kalpana Rajouriya, Ajay Taneja

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The objective of the present study is to investigate the regional and lobar deposition of size-segregated PM in respiratory tract of human body. PM in different fractions is monitored using the Grimm portable environmental dust monitor during winter season in Firozabad; a Glass city of India. PM10 concentration (200.817g/m³) was 4.46 and 2.0 times higher than the limits prescribed by WHO (45g/m⁻³) and NAAQS (100g/m⁻³) government agencies. PM2.5 concentration (83.538 g/m3) was 5.56 and 1.39 times higher from WHO (15g/m-3) and NAAQS (60g/m⁻³) limits. Results inferred that PM10 and PM2.5 was highest deposited in head region (0.3477-0.5622 & 0.366-0.4704) followed by pulmonary region, especially in the 9-21year old persons. The variation in deposition percentage in our study is mainly due to the airway geometry, PM size, and its deposition mechanisms. The coarse fraction, due to its large size, cannot follow the airway path and mostly gets deposited by inertial impaction in the head region and its bifurcations. The present study results inferred that Coarse and fine PM deposition was highly visualized in 9 (8.45610⁻⁴ g, 2.91110⁻⁴g) year and 3 (1.49610⁻⁴ g, 8.59310⁻⁵g) month age category. So, the 9year children and 3month infants category have high level of health risk.

Keywords: particulate matter, MPPD model, regional deposition, lobar deposition, health risk

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Authors: William Li

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Lane-change path planning is a crucial and yet complex task in autonomous driving. The traditional path planning approach based on a system of carefully-crafted rules to cover various driving scenarios becomes unwieldy as more and more rules are added to deal with exceptions and corner cases. This paper proposes to divide the entire path planning to two stages. In the first stage the ego vehicle travels longitudinally in the source lane to reach a safe state. In the second stage the ego vehicle makes lateral lane-change maneuver to the target lane. The paper derives the safe state conditions based on lateral lane-change maneuver calculation to ensure collision free in the second stage. To determine the acceleration sequence that minimizes the time to reach a safe state in the first stage, the paper proposes three schemes, namely, kinetic model based optimization, deep reinforcement learning, and 5G vehicle-to-vehicle (V2V) communications. The paper investigates these schemes via simulation. The model-based optimization is sensitive to the model assumptions. The deep reinforcement learning is more flexible in handling scenarios beyond the model assumed by the optimization. The 5G V2V eliminates uncertainty in predicting future behaviors of surrounding vehicles by sharing driving intents and enabling cooperative driving.

Keywords: lane change, path planning, autonomous driving, deep reinforcement learning, 5G, V2V communications, connected vehicles

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Authors: Vineeth Siripuram, Abhineet Nigam

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A bioreactor is an engineered system that supports a biologically active environment. Along the years, the advancements in bioreactors have been widely accepted all over the world for varied applications ranging from sewage treatment to tissue cloning. Driven by tissue and organ shortage, tissue engineering has emerged as an alternative to transplantation for the reconstruction of lost or damaged organs. In this study, Computational fluid dynamics (CFD) has been used to model porous medium flow in scaffolds (taken from the literature) with different flow patterns. A detailed analysis of different scaffold geometries and their influence on cell deposition in the perfusion system is been carried out using Computational fluid dynamics (CFD). Considering the fact that, the scaffold should mimic the organs or tissues structures in a three-dimensional manner, certain assumptions were made accordingly. The research on scaffolds has been extensively carried out in different bioreactors. However, there has been less focus on the morphology of the scaffolds and the flow patterns in which the perfusion system is laid upon. The objective of this paper is to employ a computational approach using CFD simulation to determine the optimal morphology and the anisotropic measurements of the various samples of scaffolds. Using predictive computational modelling approach, variables which exert dominant effects on the cell deposition within the scaffold were prioritised and corresponding changes in morphology of scaffold and flow patterns in the perfusion systems are made. A Eulerian approach was carried on in multiple CFD simulations, and it is observed that the morphological and topological changes in the scaffold perfusion system are of great importance in the commercial applications of scaffolds.

Keywords: cell seeding, CFD, flow patterns, modelling, perfusion systems, scaffold

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Authors: Tracey Bone

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Deaf inmates, whose first or preferred language is a Signed Language, experience barriers to accessing the necessary two-way communication with correctional staff, and the educational and social programs that will enhance their eligibility for conditional release from the federal prison system in Canada. The development of visual content to enhance the knowledge and skill development of correctional staff is a contemporary strategy intended to significantly improve the correctional experience for deaf inmates. This presentation reports on the development of two distinct training videos created to enhance staff’s understanding of the needs of deaf inmates; one a two-part simulation of an interaction with a deaf inmate, the second an interview with a deaf academic. Part one of video one demonstrates the challenges and misunderstandings inherent in communicating across languages without a qualified sign language interpreter; the second part demonstrates the ease of communication when communication needs are met. Video two incorporates the experiences of a deaf academic to provide the cultural grounding necessary to educate staff in the unique experiences associated with being a visual language user. Lack of staff understanding or awareness of deaf culture and language must not be acceptable reasons for the inadequate treatment of deaf visual language users in federal prisons. This paper demonstrates a contemporary approach to meeting the human rights and needs of this unique and often ignored inmate subpopulation. The deaf community supports this visual approach to enhancing staff understanding of the unique needs of this population. A study of its effectiveness is currently underway.

Keywords: accommodations, American Sign Language (ASL), deaf inmates, sensory deprivation

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Authors: William Chung

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This paper is to study the use of multiple subproblems in Dantzig-Wolfe decomposition of linear programming (DW-LP). Traditionally, the decomposed LP consists of one LP master problem and one LP subproblem. The master problem and the subproblem is solved alternatively by exchanging the dual prices of the master problem and the proposals of the subproblem until the LP is solved. It is well known that convergence is slow with a long tail of near-optimal solutions (asymptotic convergence). Hence, the performance of DW-LP highly depends upon the number of decomposition steps. If the decomposition steps can be greatly reduced, the performance of DW-LP can be improved significantly. To reduce the number of decomposition steps, one of the methods is to increase the number of proposals from the subproblem to the master problem. To do so, we propose to add a quadratic approximation function to the LP subproblem in order to develop a set of approximate-LP subproblems (multiple subproblems). Consequently, in each decomposition step, multiple subproblems are solved for providing multiple proposals to the master problem. The number of decomposition steps can be reduced greatly. Note that each approximate-LP subproblem is nonlinear programming, and solving the LP subproblem must faster than solving the nonlinear multiple subproblems. Hence, using multiple subproblems in DW-LP is the tradeoff between the number of approximate-LP subproblems being formed and the decomposition steps. In this paper, we derive the corresponding algorithms and provide some simple computational results. Some properties of the resulting algorithms are also given.

Keywords: approximate subproblem, Dantzig-Wolfe decomposition, large-scale models, multiple subproblems

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Authors: Anusha Konduru, Zhiyong Shan, Preethi Santhanam, Vinod Namboodiri, Rajiv Bagai

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Mobile applications are increasing in a significant amount, each to address the requirements of many users. However, the quick developments and enhancements are resulting in many underlying defects. Android apps create and handle a large variety of 'instance' data that has to persist across runs, such as the current navigation route, workout results, antivirus settings, or game state. Due to the nature of Android, an app can be paused, sent into the background, or killed at any time. If the instance data is not saved and restored between runs, in addition to data loss, partially-saved or corrupted data can crash the app upon resume or restart. However, it is difficult for the programmer to manually test this issue for all the activities. This results in the issue of data loss that the data entered by the user are not saved when there is any interruption. This issue can degrade user experience because the user needs to reenter the information each time there is an interruption. Automated testing to detect such data loss is important to improve the user experience. This research proposes a tool, DroidDL, a data loss detector for Android, which detects the instance data loss from a given android application. We have tested 395 applications and found 12 applications with the issue of data loss. This approach is proved highly accurate and reliable to find the apps with this defect, which can be used by android developers to avoid such errors.

Keywords: Android, automated testing, activity, data loss

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Authors: Mohammad Izadkhah, Mojtaba Hoseini, Alireza Khalili Tehrani

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In this paper we presented a new method for tracking flying targets in color video sequences based on contour and kernel. The aim of this work is to overcome the problem of losing target in changing light, large displacement, changing speed, and occlusion. The proposed method is made in three steps, estimate the target location by particle filter, segmentation target region using neural network and find the exact contours by greedy snake algorithm. In the proposed method we have used both region and contour information to create target candidate model and this model is dynamically updated during tracking. To avoid the accumulation of errors when updating, target region given to a perceptron neural network to separate the target from background. Then its output used for exact calculation of size and center of the target. Also it is used as the initial contour for the greedy snake algorithm to find the exact target's edge. The proposed algorithm has been tested on a database which contains a lot of challenges such as high speed and agility of aircrafts, background clutter, occlusions, camera movement, and so on. The experimental results show that the use of neural network increases the accuracy of tracking and segmentation.

Keywords: video tracking, particle filter, greedy snake, neural network

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Authors: Bahram Khan, Anderson Rocha Ramos, Rui R. Paulo, Fernando J. Velez

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With the growing demand for a new blend of applications, the users dependency on the internet is increasing day by day. Mobile internet users are giving more attention to their own experiences, especially in terms of communication reliability, high data rates and service stability on move. This increase in the demand is causing saturation of existing radio frequency bands. To address these challenges, researchers are investigating the best approaches, Carrier Aggregation (CA) is one of the newest innovations, which seems to fulfill the demands of the future spectrum, also CA is one the most important feature for Long Term Evolution - Advanced (LTE-Advanced). For this purpose to get the upcoming International Mobile Telecommunication Advanced (IMT-Advanced) mobile requirements (1 Gb/s peak data rate), the CA scheme is presented by 3GPP, which would sustain a high data rate using widespread frequency bandwidth up to 100 MHz. Technical issues such as aggregation structure, its implementations, deployment scenarios, control signal techniques, and challenges for CA technique in LTE-Advanced, with consideration of backward compatibility, are highlighted in this paper. Also, performance evaluation in macro-cellular scenarios through a simulation approach is presented, which shows the benefits of applying CA, low-complexity multi-band schedulers in service quality, system capacity enhancement and concluded that enhanced multi-band scheduler is less complex than the general multi-band scheduler, which performs better for a cell radius longer than 1800 m (and a PLR threshold of 2%).

Keywords: component carrier, carrier aggregation, LTE-advanced, scheduling

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Authors: Kamel A. Elshorbagy, Mohamed A. Hussein, Rola S. Afify

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Natural gas pressure reduction is typically achieved using pressure reducing valves, where isenthalpic expansion takes place with considerable amount of wasted energy in an irreversible throttling process of the gas. Replacing gas-throttling process by an expansion process in a turbo expander (TE) converts the pressure of natural gas into mechanical energy transmitted to a loading device (i.e. an electric generator). This paper investigates the performance of a turboexpander system for power recovery at natural gas pressure reduction stations. There is a considerable temperature drop associated with the turboexpander process. Essential preheating is required, using gas fired boilers, to avoid undesirable effects of a low outlet temperature. Various system configurations were simulated by the general flow sheet simulator HYSYS and factors affecting the overall performance of the systems were investigated. Power outputs and fuel requirements were found using typical gas flow variation data. The simulation was performed for two case studies in which real input data are used. These case studies involve a domestic (commercial) and an industrial natural gas pressure reduction stations in Egypt. Economic studies of using the turboexpander system in both of the two natural gas pressure reduction stations are conducted using precise data obtained through communication with several companies working in this field. The results of economic analysis, for the two case studies, prove that using turboexpander systems in Egyptian natural gas reduction stations can be a successful project for energy conservation.

Keywords: natural gas, power recovery, reduction stations, turboexpander systems

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Authors: Sabyasachi Mukhopadhyay, Joseph Ogutu, Hans-Peter Piepho

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We aim to model dynamics of wildlife or pastoral livestock population for understanding of their population change and hence for wildlife conservation and promoting human welfare. The study is motivated by an age-sex structured population counts in different regions of Serengeti-Mara during the period 1989-2003. Developing reliable and realistic models for population dynamics of large herbivore population can be a very complex and challenging exercise. However, the Bayesian statistical domain offers some flexible computational methods that enable the development and efficient implementation of complex population dynamics models. In this work, we have used a novel Bayesian state-space model to analyse the dynamics of topi and hartebeest populations in the Serengeti-Mara Ecosystem of East Africa. The state-space model involves survival probabilities of the animals which further depend on various factors like monthly rainfall, size of habitat, etc. that cause recent declines in numbers of the herbivore populations and potentially threaten their future population viability in the ecosystem. Our study shows that seasonal rainfall is the most important factors shaping the population size of animals and indicates the age-class which most severely affected by any change in weather conditions.

Keywords: bayesian state-space model, Markov Chain Monte Carlo, population dynamics, conservation

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Authors: Waqed H. Almohammed, Mohammed Y. Fattah, Sajjad E. Rasheed

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Dynamic traffic loads cause deformation of underground pipes, resulting in vehicle discomfort. This makes it necessary to reinforce the layers of soil above underground pipes. In this study, the subbase layer was reinforced. Finite element software (PLAXIS 3D) was used to in the simulation, which includes geocell reinforcement, vehicle loading, soil layers and Glass Fiber Reinforced Plastic (GRP) pipe. Geocell reinforcement was modeled using a geogrid element, which was defined as a slender structure element that has the ability to withstand axial stresses but not to resist bending. Geogrids cannot withstand compression but they can withstand tensile forces. Comparisons have been made between the numerical models and experimental works, and a good agreement was obtained. Using the mathematical model, the performance of three different pipes of diameter 600 mm, 800 mm, and 1000 mm, and three different vehicular speeds of 20 km/h, 40 km/h, and 60 km/h, was examined to determine their impact on surface settlement and vertical pressure at the pipe crown for two cases: with and without geocell reinforcement. The results showed that, for a pipe diameter of 600 mm under geocell reinforcement, surface settlement decreases by 94 % when the speed of the vehicle is 20 km/h and by 98% when the speed of the vehicle is 60 km/h. Vertical pressure decreases by 81 % when the diameter of the pipe is 600 mm, while the value decreases to 58 % for a pipe with diameter 1000 mm. The results show that geocell reinforcement causes a significant and positive reduction in surface settlement and vertical stress above the pipe crown, leading to an increase in pipe safety.

Keywords: dynamic loading, finite element, geocell-reinforcement, GRP pipe, PLAXIS 3D, surface settlement

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Authors: Berat Krasniqi, Shpëtim Thaçi, Miribane Dërmaku-Sopjani, Sokol Abazi, Mentor Sopjani

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The Mediterranean native plant, Sideritis raeseri Boiss. & Heldr. (Lamiaceae), also known as "mountain tea," has a long history of use in traditional medicine. The effects of an ethanol extract of Sideritis raeseri (SR) on uterus smooth muscle activity are evaluated in this study, and the underlying mechanism is identified. S. raeseri extract (SRE) was made from air-dried components of the SR shoot system. At 37°C, the SRE (0.5-2 mg/mL) was tested on isolated rabbit uterus rings that were suspended in a Krebs solution-filled organ bath and bubbled with a mixture of 95% O₂ and 5% CO₂. The SRE alone relaxed the muscle contraction in a concentration-dependent manner in uterine rings in in vitro tests. SRE also decreased Ca²⁺-induced contractions in the uterus by a large amount when the uterus was depolarized with carbachol (CCh, 1µM), K⁺ (80 mM), or contracted by oxytocin (5 nM). The potential involvement of NO-dependent or independent cGMP mechanisms in the uterine actions of SR was investigated. For this purpose, L-NAME (NO synthase inhibitor, 100 M) or bradykinin (NO synthase stimulator, 100 nM), or indomethacin (cyclooxygenase inhibitor, 10µM) decreased the impact of SRE. These results suggest that NO-dependent signaling is involved in SRE's mediated uterine relaxant effect. Data suggests that SRE could be a powerful tocolytic agent that reduces uterine activity and could be used to treat a number of uterine conditions.

Keywords: Sideritis raeseri, uterus, alternative medicine, intracellular mechanisms

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Authors: S. Chandrasekaran, P. A. Kiran

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Increasing demand for large-sized Floating, Storage and Regasification Units (FSRUs) for oil and gas industries led to the development of novel geometric form of Buoyant Leg Storage and Regasification Platform (BLSRP). BLSRP consists of a circular deck supported by six buoyant legs placed symmetrically with respect to wave direction. Circular deck is connected to buoyant legs using hinged joints, which restrain transfer of rotational response from the legs to deck and vice-versa. Buoyant legs are connected to seabed using taut moored system with high initial pretension, enabling rigid body motion in vertical plane. Encountered environmental loads induce dynamic tether tension variations, which in turn affect stability of the platform. The present study investigates Mathieu stability of BLSRP under the postulated tether pullout cases by inducing additional tension in the tethers. From the numerical studies carried out, it is seen that postulated tether pullout on any one of the buoyant legs does not result in Mathieu type instability even under excessive tether tension. This is due to the presence of hinged joints, which are capable of dissipating the unbalanced loads to other legs. However, under tether pullout of consecutive buoyant legs, Mathieu-type instability is observed.

Keywords: offshore platforms, stability, postulated failure, dynamic tether tension

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Authors: Raúl González Muñoz, Essam Shehab, Martin Weinitzke, Chris Fowler, Paul Baguley

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Software applications have become crucial to the aerospace industry, providing a wide range of functionalities and capabilities used during the design, manufacturing and support of aircraft. However, as this criticality increases, so too does the risk for business operations when facing a software failure. Hence, there is a need for new methodologies to be developed to support aerospace companies in effectively managing their software portfolios, avoiding the hazards of business disruption and additional costs. This paper aims to provide a definition of operational software maturity, and how this can be used to assess software operational behaviour, as well as a view on the different aspects that drive software maturity within the aerospace industry. The key research question addressed is, how can operational software maturity monitoring assist the aerospace industry in effectively managing large software portfolios? This question has been addressed by conducting an in depth review of current literature, by working closely with aerospace professionals and by running an industry case study within a major aircraft manufacturer. The results are a software maturity model composed of a set of drivers and a prototype tool used for the testing and validation of the research findings. By utilising these methodologies to assess the operational maturity of software applications in aerospace, benefits in maintenance activities and operations disruption avoidance have been observed, supporting business cases for system improvement.

Keywords: aerospace, software lifecycle, software maintenance, software maturity

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Authors: Ghodhbani Salah, Jemili Farah

Abstract:

With the growth of cyber attacks, information safety has become an important issue all over the world. Many firms rely on security technologies such as intrusion detection systems (IDSs) to manage information technology security risks. IDSs are considered to be the last line of defense to secure a network and play a very important role in detecting large number of attacks. However the main problem with today’s most popular commercial IDSs is generating high volume of alerts and huge number of false positives. This drawback has become the main motivation for many research papers in IDS area. Hence, in this paper we present a data mining technique to assist network administrators to analyze and reduce false positive alarms that are produced by an IDS and increase detection accuracy. Our data mining technique is unsupervised clustering method based on hybrid ANT algorithm. This algorithm discovers clusters of intruders’ behavior without prior knowledge of a possible number of classes, then we apply K-means algorithm to improve the convergence of the ANT clustering. Experimental results on real dataset show that our proposed approach is efficient with high detection rate and low false alarm rate.

Keywords: intrusion detection system, alarm filtering, ANT class, ant clustering, intruders’ behaviors, false alarms

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Authors: S. K. Fasogbon, O. E. Oguegbu

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Pasteurization is one of the most important steps in the preservation of beer products, which improves its shelf life by inactivating almost all the spoilage organisms present in it. However, there is no gain saying the fact that it is always difficult to determine the slowest heating zone, the temperature profile and pasteurization units inside bottled beer during pasteurization, hence there had been significant experimental and ANSYS fluent approaches on the problem. This work now developed Computational fluid dynamics model using COMSOL Multiphysics. The model was simulated to determine the slowest heating zone, temperature profile and pasteurization units inside the bottled beer during the pasteurization process. The results of the simulation were compared with the existing data in the literature. The results showed that, the location and size of the slowest heating zone is dependent on the time-temperature combination of each zone. The results also showed that the temperature profile of the bottled beer was found to be affected by the natural convection resulting from variation in density during pasteurization process and that the pasteurization unit increases with time subject to the temperature reached by the beer. Although the results of this work agreed with literatures in the aspects of slowest heating zone and temperature profiles, the results of pasteurization unit however did not agree. It was suspected that this must have been greatly affected by the bottle geometry, specific heat capacity and density of the beer in question. The work concludes that for effective pasteurization to be achieved, there is a need to optimize the spray water temperature and the time spent by the bottled product in each of the pasteurization zones.

Keywords: modeling, heat transfer, temperature profile, pasteurization process, bottled beer

Procedia PDF Downloads 192

Authors: Nina Tsvetkova, Rumen Harizanov, Aleksandra Ivanova, Iskra Rainova, Nina Yancheva-Petrova, Dimitar Strashimirov, Raina Enikova, Mihaela Videnova, Eleonora Kaneva, Iskren Kaftandjiev, Viktoria Levterova, Ivan Simeonovski, Nikolay Yanev, Georgi Hinkov

Abstract:

The Pneumocystis jirovecii (P. jirovecii) and protozoan of the genera Acanthamoeba, Cryptosporidium, and Toxoplasma gondii are opportunistic pathogens that can cause life-threatening infections in immunocompromised patients. Aim of the study was to evaluate the coinfection rate with opportunistic protozoal agents among Bulgarian patients diagnosed with P. jirovecii pneumonia. Thirty-eight pulmonary samples were collected from 38 patients (28 HIV-infected) with P. jirovecii infection. P. jirovecii DNA was detected by real-time PCR targeting the large mitochondrial subunit ribosomal RNA gene. Acanthamoeba was determined by genus-specific conventional PCR assay. Real-time PCR for the detection of a Toxoplasma gondii and Cryptosporidium DNA fragment was used. Pneumocystis DNA was detected in all 38 specimens; 28 (73.7%) were from HIV-infected patients. Three (10,7%) of them were co-infected with T. gondii and 1 (3.6%) with Cryptosporidium. In the group of non-HIV-infected (n=10), Cryptosporidium DNA was detected in an infant (10%). Acanthamoeba DNA was not found in the tested samples. The current study showed a relatively low rate of coinfections of Cryptosporidium spp./T. gondii and P. jirovecii in the Bulgarian patients studied.

Keywords: coinfection, opportunistic protozoal agents, Pneumocystis jirovecii, pulmonary infections

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Authors: Sudarshan Kalsulkar, Sunil S. Bhagwat

Abstract:

Biosorption is a physiochemical process that occurs naturally in certain biomass which allows it to passively concentrate and bind contaminants onto its cellular structure. Activated carbons (AC) are one such efficient biosorbents made by utilizing lignocellulosic materials from agricultural waste. Steam activated carbon (AC) was synthesized from Barley husk. Its synthesis parameters of time and temperature were optimized. Its physico-chemical properties like density, surface area, pore volume, Methylene blue and Iodine values were characterized. BET surface area was found to be 42 m²/g. Batch Adsorption tests were carried out to determine the maximum adsorption capacity (qmax) for various metal ions. Cd+2 48.74 mg/g, Pb+2 19.28 mg/g, Hg+2 39.1mg/g were the respective qmax values. pH and time were optimized for adsorption of each ion. Column Adsorptions were carried for each to obtain breakthrough data. Microbial adsorption was carried using E. coli K12 strain. 78% reduction in cell count was observed at operating conditions. Thus the synthesized Barley husk AC can be an economically feasible replacement for commercially available AC prepared from the costlier coconut shells. Breweries and malting industries where barley husk is a primary waste generated on a large scale can be a good source for bulk raw material.

Keywords: activated carbon, Barley husk, biosorption, decontamination, heavy metal removal, water treatment

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Authors: Iván González, Pablo Andrés Maya, Sebastián Jaén

Abstract:

Eco-industrial parks (EIPs) work as networks for the exchange of by-products, such as materials, water, or energy. This research identifies the relevant factors in the formation of EIPs in different industrial environments around the world. Then an aggregation of these factors is carried out to reduce them from 50 to 17 and classify them according to 5 fundamental axes. Subsequently, the Vester Sensitivity Model (VSM) systemic methodology is used to determine the influence of the 17 factors on an EIP system and the interrelationship between them. The results show that the sequence of effects between factors: Trust and Cooperation → Business Association → Flows → Additional Income represents the “backbone” of the system, being the most significant chain of influences. In addition, the Organizational Culture represents the turning point of the Industrial Symbiosis on which it must act correctly to avoid falling into unsustainable economic development. Finally, the flow of Information should not be lost since it is what feeds trust between the parties, and the latter strengthens the system in the face of individual or global imbalances. This systemic understanding will enable the formulation of pertinent policies by the actors that interact in the formation and permanence of the EIP. In this way, it seeks to promote large-scale sustainable industrial development, integrating various community actors, which in turn will give greater awareness and appropriation of the current importance of sustainability in industrial production.

Keywords: critical factors, eco-industrial park, industrial symbiosis, system methodology

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