Study of Cryogenic Vaporization Source-Term Due to Heat Transfer from the Solid Substrate

U.S. regulation requires LNG facilities to demonstrate a safe exclusion zone for public safety. European safety case also requires that the facility will demonstrate their risk level within a tolerable limit. Thus, cryogenic liquids (i.e., LNG) release scenarios needs to be modeled to determine consequence severity and perceived risk level. The existing models and tools are very sensitive to the inputs, also known as source-terms. Inaccurate inputs might result in an amplified or subdued consequence severity and may change the estimated risk level and/or safety exclusion zone. Accurate prediction of the source-terms is complex due to the presence of boiling regimes and requires validated models of boiling regimes. A CFD-based approach is taken to model film boiling using Rayleigh-Taylor instability and volume of fluid (VOF) methods. Film boiling simulations for LN2, LO2, and LNG are conducted with a various degree of wall superheat. The simulated results were compared with Berenson and Klimenko correlations to demonstrate that CFD model overcomes the limitations of these correlations. To extend the applicability of these simulations, a first principle model is proposed to enable a faster calculation of heat transfer to cryogenic pool boiling. Medium-scale cryogenic spill experiments have been conducted on an instrumented concrete substrate where LN2, LO2, and liquid air are used. The vaporization rate, temperature, and heat flux profiles are recorded during the experiments. It is found that the effect of the mixture on the LN2 vaporization rate is not significant and the heat conduction inside the concrete substrate is unidirectional. The proposed CFD-based film boiling models for LN2 and LO2 are validated using medium-scale experimental data and are in agreement for higher wall superheats but slightly deviates for the lower wall superheats. The deviation in experimental data can be attributed to the surface roughness and change in boiling regime from film to nucleate. The model for LNG is validated against the experimental data reported in the literature. It is found that the model can capture the vaporization rate reported from the Maplin Sands experiments and other laboratory tests on film boiling

Effects of application parameters, film thickness, and dehydration oven temperature on automotive waterborne basecoat popping

Application parameters: flow rate, target distance, film thickness, and dehydration oven temperature were investigated in relation to automotive waterborne basecoat popping. The study was conducted in three phases using melamine cross-linked acrylic latex basecoat and acrylic acid/epoxy clear coat. All experiments were conducted at the Automotive Research and Development Centre in Windsor, Ontario, Canada. First, a relationship among tip speed, flow rate, and film thickness was established, which was used later to set constant film thickness. The Phase-1 study indicates that flow rate and target distance are not significant in relation to popping. A complementary analysis indicates that film thickness significantly affects popping. Film thickness was also found significant in relation to popping from the Phase-2 study. The Phase-3 study indicates that dehydration oven temperature and film thickness significantly affects popping. Although this study identified the factors causing waterborne basecoat popping,conditions for a pop-free paint surface were not achieved

Delphi Technique in Poverty Alleviation: A Case Study

This study aims at investigating scholars thinking intended for poverty alleviation. Two-round Delphi techniques were applied to attain experts’ opinion in support of probable resolution of poverty. Government officials, Non-government executives, University academics, and social & political personalities are considered as scholars. The scholars think that limitation of job is the fundamental cause of poverty that is why the poor are bound to live in vulnerable unhygienic places where inadequate services are prevailing. They also argued that by providing home-based work and especial training that will help them to get job for income generation, the poverty problems could be reduced. As well community-based management similar to labor intensive low-cost housing factory and sanitation plant will also been lead to decrease poverty. To avoid hypothetical discover, the study analyzed poverty alleviation activities of UNDP/GOB project. The UNDP/GOB project entitled ‘Local Partnerships for Urban Poverty Alleviation’ is one of the biggest urban poverty alleviating projects in Bangladesh. There are many successful activities of this project such as community-based micro-credit, sanitation as well as drinking water has been highlighted. The study was undertaken by acquiring primary data from the field survey that employed a structure questionnaire and gathered information emphasis on poverty. Heads of poor households or a member behalf of HH, were used as respondents.

Identification of Key Proteins Involved in Axon Guidance Related Disorders: A Systems Biology Approach

Axon guidance is a crucial process for growth of the central and peripheral nervous systems. In this study, 3 axon guidance related disorders, namely- Duane Retraction Syndrome (DRS) , Horizontal Gaze Palsy with Progressive Scoliosis (HGPPS) and Congenital fibrosis of the extraocular muscles type 3 (CFEOM3) were studied using various Systems Biology tools to identify the genes and proteins involved with them to get a better idea about the underlying molecular mechanisms including the regulatory mechanisms. Based on the analyses carried out, 7 significant modules have been identified from the PPI network. Five pathways/processes have been found to be significantly associated with DRS, HGPPS and CFEOM3 associated genes. From the PPI network, 3 have been identified as hub proteins- DRD2, UBC and CUL3

Sensor Fusion and Non-linear MPC controller development studies for Intelligent Autonomous vehicular systems

The demand for safety and fuel efficiency on ground vehicles and advancement in embedded systems created the opportunity to develop Autonomous controller. The present thesis work is three fold and it encompasses all elements that are required to prototype the autonomous intelligent system including simulation, state handling and real time implementation. The Autonomous vehicle operation is mainly dependent upon accurate state estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement. The initial work focuses on alleviating magnetic disturbances for ground vehicles by fusing the vehicle kinematics information with IMU senor in an Extended Kalman filter (EKF) with the vehicle orientation represented using Quaternions. The previous studies covers the handling of sensor noise data for vehicle yaw estimations and the same approach can be applied for additional sensors used in the work. However, it is important to develop simulations to analyze the autonomous navigation for various road, obstacles and grade conditions. These simulations serve base platform for real time implementation and provide the opportunity to implement it on real road vehicular application and leads to prototype the controller. Therefore, the next section deals with simulations that focuses on developing Non-linear Model Predictive controller for high speed off-road autonomous vehicle, which avoids undesirable conditions including stationary obstacles, moving obstacles and steep regions while maintaining the vehicle safety from rollover. The NMPC controller is developed using CasADi tools in MATLAB environment. As mentioned, the above two sections provide base platform for real time implementation and the final section uses these techniques for developing intelligent autonomous vehicular system that would track the given path and avoid static obstacles by rejecting the considerable environmental disturbance in the given path. The Linear Quadratic Gaussian (LQG) is developed for the present application, The model developed in the LQG controller is a kinematic bicycle model, that mimics 1/5th scale truck and cubic spline has been used to connect and generate the continuous target path

WA Agricultural Growth and the State Economy Research Report Volume 1

The impact of state and national policies on the Western Australian (WA) agriculture can not be assessed realistically unless the distinctive characteristics of WA agriculture are taken into account. The objective of this paper is to develop and document an economic information base for WA agriculture by identifying and exploring the main characteristics of its production systems. The study reveals that agriculture plays a more important role in WA than in the rest of Australia (ROA). The share of WA in the national gross value of agricultural production and exports is much higher than her share in the national gross domestic product. The structure of agriculture in terms of product mix is different for WA than for other states in Australia. The farm structure and the overall farming practices in WA are also different from the ROA. The production concentration and product-mix of WA agriculture seem to be very much influenced by the rainfall patterns and topographic conditions

The acquisition and use of information to make investment decisions: a study of financial analysts in China

This study draws upon semi-structured interviews with 23 Chinese financial analysts (Beijing, Shanghai) to examine how they improve their information comprehension through private information search and use such information to evaluate company value. Despite working in state-owned institutions, Chinese financial analysts claim that their analytical framework is similar to their counterparty in the Western setting. However, our results indicate that Chinese analyst's analytical practice is considerably influenced by the specific characteristics of the Chinese listed company both in information acquisition and usage. Private meetings are regarded as a 'value added' information source providing price-sensitive information including product cost structure and possible assets injection from the listed company principle shareholders

Real-time crash prediction of urban highways using machine learning algorithms

Doctor of PhilosophyDepartment of Civil EngineeringEric J. FitzsimmonsMotor vehicle crashes in the United States continue to be a serious safety concern for state highway agencies, with over 30,000 fatal crashes reported each year. The World Health Organization (WHO) reported in 2016 that vehicle crashes were the eighth leading cause of death globally. Crashes on roadways are rare and random events that occur due to the result of the complex relationship between the driver, vehicle, weather, and roadway. A significant breadth of research has been conducted to predict and understand why crashes occur through spatial and temporal analyses, understanding information about the driver and roadway, and identification of hazardous locations through geographic information system (GIS) applications. Also, previous research studies have investigated the effectiveness of safety devices designed to reduce the number and severity of crashes. Today, data-driven traffic safety studies are becoming an essential aspect of the planning, design, construction, and maintenance of the roadway network. This can only be done with the assistance of state highway agencies collecting and synthesizing historical crash data, roadway geometry data, and environmental data being collected every day at a resolution that will help researchers develop powerful crash prediction tools. The objective of this research study was to predict vehicle crashes in real-time. This exploratory analysis compared three well-known machine learning methods, including logistic regression, random forest, support vector machine. Additionally, another methodology was developed using variables selected from random forest models that were inserted into the support vector machine model. The study review of the literature noted that this study’s selected methods were found to be more effective in terms of prediction power. A total of 475 crashes were identified from the selected urban highway network in Kansas City, Kansas. For each of the 475 identified crashes, six no-crash events were collected at the same location. This was necessary so that the predictive models could distinguish a crash-prone traffic operational condition from regular traffic flow conditions. Multiple data sources were fused to create a database including traffic operational data from the KC Scout traffic management center, crash and roadway geometry data from the Kanas Department of Transportation; and weather data from NOAA. Data were downloaded from five separate roadway radar sensors close to the crash location. This enable understanding of the traffic flow along the roadway segment (upstream and downstream) during the crash. Additionally, operational data from each radar sensor were collected in five minutes intervals up to 30 minutes prior to a crash occurring. Although six no-crash events were collected for each crash observation, the ratio of crash and no-crash were then reduced to 1:4 (four non-crash events), and 1:2 (two non-crash events) to investigate possible effects of class imbalance on crash prediction. Also, 60%, 70%, and 80% of the data were selected in training to develop each model. The remaining data were then used for model validation. The data used in training ratios were varied to identify possible effects of training data as it relates to prediction power. Additionally, a second database was developed in which variables were log-transformed to reduce possible skewness in the distribution. Model results showed that the size of the dataset increased the overall accuracy of crash prediction. The dataset with a higher observation count could classify more data accurately. The highest accuracies in all three models were observed using the dataset of a 1:6 ratio (one crash event for six no-crash events). The datasets with1:2 ratio predicted 13% to 18% lower than the 1:6 ratio dataset. However, the sensitivity (true positive prediction) was observed highest for the dataset of a 1:2 ratio. It was found that reducing the response class imbalance; the sensitivity could be increased with the disadvantage of a reduction in overall prediction accuracy. The effects of the split ratio were not significantly different in overall accuracy. However, the sensitivity was found to increase with an increase in training data. The logistic regression model found an average of 30.79% (with a standard deviation of 5.02) accurately. The random forest models predicted an average of 13.36% (with a standard deviation of 9.50) accurately. The support vector machine models predicted an average of 29.35% (with a standard deviation of 7.34) accurately. The hybrid approach of random forest and support vector machine models predicted an average of 29.86% (with a standard deviation of 7.33) accurately. The significant variables found from this study included the variation in speed between the posted speed limit and average roadway traffic speed around the crash location. The variations in speed and vehicle per hour between upstream and downstream traffic of a crash location in the previous five minutes before a crash occurred were found to be significant as well. This study provided an important step in real-time crash prediction and complemented many previous research studies found in the literature review. Although the models investigate were somewhat inconclusive, this study provided an investigation of data, variables, and combinations of variables that have not been investigated previously. Real-time crash prediction is expected to assist with the on-going development of connected and autonomous vehicles as the fleet mix begins to change, and new variables can be collected, and data resolution becomes greater. Real-time crash prediction models will also continue to advance highway safety as metropolitan areas continue to grow, and congestion continues to increase