7264 research outputs found
Sort by
CHARACTERIZATION AND PERFORMANCE ASSESSMENT OF A NOVEL NIO-FE3O4-POLYTHIOPHENE NANOCOMPOSITE FOR ASPHALTENE PRECIPITATION INHIBITION
No full textPrecipitation and deposition of asphaltene represents a significant challenge in the oil industry. Nanomaterials are considered as proper candidates for asphaltene adsorption and precipitation owing to their exceptional physical and chemical features. In this dissertation, first, a novel NiO-Fe3O4-Polythiophene nanocomposite (NC) was characterized using various advanced analytical methods to ensure its authenticity. X-ray diffraction (XRD) was used to determine the crystallite size and explore structures of the NC. Scanning electron microscopy (SEM) was used to investigate surface morphology and assess the particle size of the NC qualitatively. Fourier transform infrared spectroscopy (FTIR) methods was used to identify functional groups and elemental bonding of the NC. Brunauer-Emmett-Teller (BET) method was used to determine surface area of the NC. Thermogravimetric analyzer (TGA) was used to explore thermal stability of the NC. Using the XRD data the crystallite size was determined 33.2 nm. The particle size of the NC ranges from 60 to 400 nm based on SEM images, and surface area of the NC was determined 55.83 m2/g using the BET test data. TGA analysis revealed that the NC is thermally stable with a negligible mass loss under reservoir conditions (80°C). To assess efficacy of the novel NC for adsorption and inhibition of asphaltene, UV-spectroscopy technique was used to determine Asphaltene Onset Point (AOP) in presence and absence of the NC and then supernatant obtained from TGA analysis was used for adsorption kinetics isotherm modeling. Adsorption kinetics isotherm modeling was done using the Langmuir (R2 = 0.98) and Freundlich (R2 = 0.95) isotherm models. The experimental data matched well both models which suggests monolayer and multilayer adsorption behavior for adsorption of asphaltene onto the surface of the NC. A maximum adsorption capacity of 1.116 mg/m2 was obtained for the NC. TGA analysis confirmed that oxidation of virgin asphaltene started at around 400-450℃; while oxidation of 5,000 ppm sample with NC started at around 350℃. The NC has catalyzed oxidation of the asphaltene. An optimum NC concentration of 0.3 wt% was obtained and an AOP shifting from 40% to 48% volume of n-heptane was observed for the optimum concentration. The outcomes prove that, the novel NC is an effective nano-inhibitor for asphaltene under laboratory conditions
DESIGN AND ANALYSIS OF A SMALL CARGO UAV
No full textThis Capstone Project focuses on taking an engineering approach to designing a small cargo (2 kg payload) all-electric UAV that has the purpose of solving the problem of inaccessibility of medical aid in distant, hard-to-reach areas of Kazakhstan. Having set up exact parameter requirements for an UAV according to the mission, it is possible to define required dimensions and configurations of an UAV. It is decided to use a launcher to enable the UAV to take-off and the landing is to be done by belly landing. Analysis includes calculations, statistical data analysis, computer aided modeling and simulations
Edge-assisted Human Action Recognition
No full textThe project addresses the significant challenges posed by the vast amount of video data generated by Internet of Things (IoT) devices, especially surveillance cameras, by developing an edge-assisted human action recognition system (HAR). Utilizing edge computing and deep learning technologies, including advanced pose estimation and convolutional neural networks (CNNs), the system aims to provide real-time HAR with minimal latency and reduced reliance on cloud resources. Key components include end devices for data capture, a cloud server for model training and management, and a web application for user interaction. This integration sets a new standard in real-time, edge-assisted video analytics by tackling traditional challenges related to latency, scalability, and efficiency. The project not only progresses through stages such as dataset creation, pipeline development, and software architecture design but also demonstrates the practical application and effectiveness of these technologies in enhancing video surveillance systems
MULTISCALE/MULTIPHYSICS SIMULATION OF WIND TURBINES USING ARBITRARY HYBRID TURBULENCE MODEL AND FULLY COUPLED FSI
No full textThis paper suggests performing multi-scale and multi-physics simulations of wind turbine analysis using numerical approximations and mathematical equations. Multi-fidelity numerical simulations are becoming very common, with the importance of renewable energy increasing and demanding wind turbines. To achieve this, researchers have turned to simulation and modeling approaches to improve the turbines’ performance. This study elaborates further on the advantages and limitations of using an Arbitrary Hybrid Turbulence Model (AHTM) for the simulation of a wind turbine flow and demonstrates how fully coupled fluid-structure interaction (FSI) analysis helps to improve the simulated physical behavior of a wind turbine. On the other hand, the
quantitative approach consists of various types of numerical simulations and mathematical
equations; it emerged as an indispensable one for the methodology to get reliable and accurate
answers. Furthermore, it is a well-established fact to a great measure that the wind turbine design
community lacks good-quality analytical resources. This research seeks to address this critical
need. Here we apply a new arbitrary hybrid turbulence model (AHTM) under the DAFoam
software, an OpenFOAM derivative, to the NREL Phase VI wind turbine in order to assess its
performance against the conventional URANS model. The AHTM model demonstrated superior
accuracy compared to the URANS model. On the other hand, mesh quality improvement, higher
order schemes, and aeroelastic features of the wind turbine would further add to the accuracy of
VLES and URANS models, and thus enable an advanced FSI analysis of the wind turbine.
Secondly, the VLES capability in DAFoam was tested under two cases, the PitzDaily and the
MACH wing, as a way of applying them for verification of capability. The FSI is implemented
through the new fluid and solid solvers interfacing with an MPhys-based solution
EXTENSION OF TRANSITION PROBABILITY FORMULAS IN MULTI-SPECIES ASYMMETRIC SIMPLE EXCLUSION PROCESS
No full textThe Asymmetric Simple Exclusion Process (ASEP) is a mathematical model used
to describe the movement of particles in a one-dimensional system. This work explores
multi-species ASEP dynamics, where, in contrast to the single-species model,
additional rules regulate interactions between various particle kinds. Tracy and Widom
introduced a transition probability formula for single-species ASEP, with subsequent
advancements by Lee. Building upon these foundations, we extend Lee’s work, presenting
transition probability formulas for different initial conditions, thus expanding
our knowledge of ASEP dynamics. By explaining these equations, we improve the
understanding of matrix elements inside the ASEP framework, leading to deeper insights
across various fields of application
PRECISE VARIATIONAL CALCULATIONS OF S e , P e , AND De STATES OF FEW-ELECTRON ATOMS
No full textThanks to the advancements in modern computer technology, it is now possible to compute the spectrum of small atoms and molecules with a degree of precision that is comparable with that of high-resolution spectroscopic experiments.Theoretical formulations, derived directly from the fundamental principles of quantum mechanics, have been developed and implemented on high-performance computer (HPC) clusters for conducting large scale quantum mechanical calculations. These computational codes, including those and the formalism summarized in this dissertation, are primarily used to enhance, predict, and validate the total energies corresponding to an angular momentum state of specific systems
ROBUST DATA-DRIVEN PREDICTIVE MODEL FOR BRAIN-COMPUTER INTERFACE
No full textA Brain-Computer Interface (BCI) system enables communication and control between a user and an external device without relying on peripheral and muscular activity. Effective control of such a device hinges on accurately recognizing and decoding intricate brain activity patterns generated by the user. The goal of this PhD project is to develop a robust model for predicting human mental intentions using electroencephalography (EEG) signals. EEG, a widely used non-invasive method for monitoring brain activity, is considered due to its ethical considerations, relatively low cost, and its ability to provide a high temporal resolution of received signals. The robustness of the system is verified based on the classification accuracy with respect to the previously unknown subjects such that the performance of subject-independent (SI) BCI system could be evaluated.
An essential challenge in BCI research is developing a classifier capable of interpreting users' mental states from EEG data collected from independent subjects. The focus on SI classification is justified because it can lead to BCIs that eliminate the need for individual calibration processes. Over the past few years, deep neural networks (DNNs) in general, and in particular Convolutional Neural Networks (CNNs), have shown impressive training efficiency and performance, leading to the development of state-of-the-art architectures for accurate EEG classification. In this Thesis, to further enhance the performance of the CNN in SI classification, multi-subject ensemble CNN (MS-En-CNN) models are designed. These are the ensembles of CNN classifiers where each base classifier
is built using data aggregated from multiple subjects. Based on the distribution of subject-specific data for training and tuning the base learners of the ensemble, three design strategies for MS-En-CNN are introduced: Subject-Specific Training and Model Selection (SS-TM), Subject Pairs Training and Model Selection (SP-TM), and Delete-a-Subject-Jackknife (DASJ) approach. The predictive performance of the proposed
techniques is evaluated across two BCI paradigms, namely motor imagery (MI) and P300, using various publicly available datasets. Empirical results show that with any of the presented strategies constructing MS-En-CNN leads to a significantly better SI classification performance with respect to the average performance of the base CNN classifiers. Moreover, MS-En-CNN notably enhances average classification accuracy compared to a single CNN trained on pooled data from training subjects. Among the three strategies, the latter approach, a jackknife-inspired deep learning technique, emerges as the most promising one. It is then benchmarked against state-of-the-art methods, highlighting its superior performance in single-trial SI classification. While these results show potential for datasets with a small number of subjects, addressing computational requirements for large-scale datasets involves extending this approach through the consideration of K-fold cross-validation (CV). In this extended approach, instead of deleting a single subject to form a jackknife sample, a group of K subjects is set aside. On one of the largest MI datasets a K-fold CV-based MS-En-CNN demonstrated a statistically significant improvement (p < 0.001) over the best previously reported results. In addition to MS-En-CNN, proven as a simple yet effective method to enhance the performance of existing CNN models, a new adaptive boosting strategy on the basis of CNN base classifiers (AdaBoost-CNN) with iterative oversampling is proposed. This innovative approach is contrasted with the conventional sample reweighting method, showcasing its potential. Encouraged by promising results, the AdaBoost-CNN warrants further investigation. Overall, this study highlights the effectiveness of MS-En-CNN and AdaBoost-CNN and offers valuable insights that pave the way for further advancements in SI classification within BCI applications
APPLICATION OF FAST RESERVOIR SIMULATION CAPACITANCE-RESISTANCE METHOD TO PREDICT THE HOT WATER FLOODING PERFORMANCE
No full textA range of methods is available to assess a reservoir performance. Development and application of fast methods to evaluate the performance of a recovery method and provide a general picture of injectors/producers connectivity is critical to manage a reservoir. Capacitance Resistance Model (CRM) is a useful tool for improving real-time flood management, as it allows rapid modeling and simulation of gas and water flood recovery processes. The CRM approach is based on signal processing methods in which injection rates are accepted as input signals and production flow rates are considered as reservoir response or output signals. The model offers key advantages, including simplicity, immediate results, and optimal performance even with minimal initial data. Over recent years, enhancements in CRM have established it as a reservoir management tool, enabling essential tasks like history matching of production data, forecasting production rates, scheduling injection rates, detecting injection leakage, and estimating fracture distribution (Sayarpour, 2008).
In this study, we expanded the application of CRM to predict the behavior of hot water injection processes. Systems identification is applied for history matching using only injection/production data from commercial simulator to characterize the reservoir models where injection of hot water was applied, evaluating interwell connectivities and time constants. Four case studies were developed with two different injection fluid types. These included a homogeneous model with a five-spot well pattern (Case 1), models featuring high-permeability streaks (Case 2 and 3), and a heterogeneous reservoir model (Case 4). In these cases, bottomhole pressures and production rates remained constant, while injection rates fluctuated over the simulation period. The first three cases were analyzed to predict reservoir performance analytically under specific conditions for homogeneous scenarios. The highest calculated average error was observed during Case 2 for both total liquid production and oil production rates (10.84% and 11.79%, respectively), while the minimum average error values were found in Case 4, with values of 6.50% for liquid rates and 5.76% for oil production rates. In all cases, the results of the developed models exhibited satisfactory agreement with those of a grid-based commercial simulator. We considered these hypothetical cases where modifications were applied to generate a more reliable evaluation of interwell connectivity and time constants, and used the R-squared value of the model as a fitting parameter for history matching processes. This approach, applied across multiple cases, yielded excellent evaluations of both reservoir performance and well connectivity
TOUCH-DRIVEN OBJECT IDENTIFICATION AND MANIPULATION
No full textBeing in human environments, the robots need to know how to manipulate and interact with humans and different objects. To successfully implement this task, the prior knowledge of an object’s properties must be known. We designed a new type of tactile sensor based on the active vibro-feedback for object stiffness classification and integrated them into a gripping system of the Shadow Robotic Hand for fine manipulation. The project was logically divided into two parts. First, we designed an object classification system to categorize plastic and silicone cubes based on their stiffness properties using active tactile sensing. Then, having the prior information about the testing objects’ characteristics, the Shadow Hand was programmed to perform a rolling motion with the cubes, showing dexterity and precise control of the robotic system. The project is intended to combine signal processing and robot control to realize stiffness classification for adaptive grasp and tactile servoing for fine motion manipulation
WOMEN’S LEADERSHIP AND MOTHERHOOD IN HIGHER EDUCATION: A CASE STUDY OF WOMEN ADMINISTRATORS IN A REGIONAL UNIVERSITY IN EAST KAZAKHSTAN
No full textThis research explores the experiences of mothers who hold administrative positions in a regional university in East Kazakhstan. The study examines the challenges and benefits of balancing motherhood and administrative duties in higher education, aiming to inform the development of supportive structures for these women. This study used a qualitative method with semi-structured interviews as the data collection instrument. It delves into the challenges these mother administrators face, including work-life conflicts, workplace dynamics, and access to institutional support. The study also identifies positive aspects of having the dual role, including personal fulfillment, career advancement, and contributions to diversity within the institution. The findings highlight the significance of implementing flexible work schedules, providing leadership training and coping strategies to support female administrators. These measures can contribute to increasing job satisfaction, improving retention rates, and fostering a more inclusive and supportive work environment. This study adds to the existing body of knowledge on female leadership in higher education by providing practical insights for educational institutions to enhance their support systems for female administrators