592 research outputs found
Experimental investigations of two-phase flow measurement using ultrasonic sensors
This thesis presents the investigations conducted in the use of ultrasonic
technology to measure two-phase flow in both horizontal and vertical pipe flows
which is important for the petroleum industry. However, there are still key
challenges to measure parameters of the multiphase flow accurately. Four
methods of ultrasonic technologies were explored.
The Hilbert-Huang transform (HHT) was first applied to the ultrasound signals of
air-water flow on horizontal flow for measurement of the parameters of the two-
phase slug flow. The use of the HHT technique is sensitive enough to detect the
hydrodynamics of the slug flow. The results of the experiments are compared
with correlations in the literature and are in good agreement.
Next, experimental data of air-water two-phase flow under slug, elongated
bubble, stratified-wavy and stratified flow regimes were used to develop an
objective flow regime classification of two-phase flow using the ultrasonic
Doppler sensor and artificial neural network (ANN). The classifications using the
power spectral density (PSD) and discrete wavelet transform (DWT) features
have accuracies of 87% and 95.6% respectively. This is considerably more
promising as it uses non-invasive and non-radioactive sensors.
Moreover, ultrasonic pulse wave transducers with centre frequencies of 1MHz
and 7.5MHz were used to measure two-phase flow both in horizontal and
vertical flow pipes. The liquid level measurement was compared with the
conductivity probes technique and agreed qualitatively. However, in the vertical
with a gas volume fraction (GVF) higher than 20%, the ultrasound signals were
attenuated.
Furthermore, gas-liquid and oil-water two-phase flow rates in a vertical upward
flow were measured using a combination of an ultrasound Doppler sensor and
gamma densitometer. The results showed that the flow gas and liquid flow rates
measured are within ±10% for low void fraction tests, water-cut measurements
are within ±10%, densities within ±5%, and void fractions within ±10%. These
findings are good results for a relatively fast flowing multiphase flow
Structural Health Monitoring in Composite Structures: A Comprehensive Review.
This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented
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ReSCon '10, Research Student Conference: Book of Abstracts
The third SED Research Student Conference (ReSCon2010) was hosted over three days, 21-23 June 2010, in the Hamilton Centre at Brunel University. The conference consisted of oral and poster presentations, which showcased the high quality and diversity of the research being conducted within the School of Engineering and Design. The abstracts and presentations were the result of ongoing research by postgraduate research students from the School. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School
Oil and Gas flow Anomaly Detection on offshore naturally flowing wells using Deep Neural Networks
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Oil and Gas industry, as never before, faces multiple challenges. It is being impugned for being
dirty, a pollutant, and hence the more demand for green alternatives. Nevertheless, the world still has
to rely heavily on hydrocarbons, since it is the most traditional and stable source of energy, as opposed
to extensively promoted hydro, solar or wind power. Major operators are challenged to produce the
oil more efficiently, to counteract the newly arising energy sources, with less of a climate footprint,
more scrutinized expenditure, thus facing high skepticism regarding its future. It has to become
greener, and hence to act in a manner not required previously.
While most of the tools used by the Hydrocarbon E&P industry is expensive and has been used for
many years, it is paramount for the industry’s survival and prosperity to apply predictive maintenance
technologies, that would foresee potential failures, making production safer, lowering downtime,
increasing productivity and diminishing maintenance costs. Many efforts were applied in order to
define the most accurate and effective predictive methods, however data scarcity affects the speed
and capacity for further experimentations. Whilst it would be highly beneficial for the industry to invest
in Artificial Intelligence, this research aims at exploring, in depth, the subject of Anomaly Detection,
using the open public data from Petrobras, that was developed by experts.
For this research the Deep Learning Neural Networks, such as Recurrent Neural Networks with LSTM
and GRU backbones, were implemented for multi-class classification of undesirable events on naturally
flowing wells. Further, several hyperparameter optimization tools were explored, mainly focusing on
Genetic Algorithms as being the most advanced methods for such kind of tasks.
The research concluded with the best performing algorithm with 2 stacked GRU and the following
vector of hyperparameters weights: [1, 47, 40, 14], which stand for timestep 1, number of hidden units
47, number of epochs 40 and batch size 14, producing F1 equal to 0.97%.
As the world faces many issues, one of which is the detrimental effect of heavy industries to the
environment and as result adverse global climate change, this project is an attempt to contribute to
the field of applying Artificial Intelligence in the Oil and Gas industry, with the intention to make it
more efficient, transparent and sustainable
NASA SBIR abstracts of 1991 phase 1 projects
The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included
Reports about 8 selected benchmark cases of model hierarchies : Deliverable number: D5.1 - Version 0.1
Based on the multitude of industrial applications, benchmarks for model hierarchies will be created that will form a basis for the interdisciplinary research and for the training programme. These will be equipped with publically available data and will be used for training in modelling, model testing, reduced order modelling, error estimation, efficiency optimization in algorithmic approaches, and testing of the generated MSO/MOR software. The present document includes the description about the selection of (at least) eight benchmark cases of model hierarchies.EC/H2020/765374/EU/Reduced Order Modelling, Simulation and Optimization of Coupled Systems/ROMSO
A Review of Structural Health Monitoring Techniques as Applied to Composite Structures.
Structural Health Monitoring (SHM) is the process of collecting, interpreting, and analysing data from structures in order to determine its health status and the remaining life span. Composite materials have been extensively use in recent years in several industries with the aim at reducing the total weight of structures while improving their mechanical properties. However, composite materials are prone to develop damage when subjected to low to medium impacts (ie 1 – 10 m/s and 11 – 30 m/s respectively). Hence, the need to use SHM techniques to detect damage at the incipient initiation in composite materials is of high importance. Despite the availability of several SHM methods for the damage identification in composite structures, no single technique has proven suitable for all circumstances. Therefore, this paper offers some updated guidelines for the users of composites on some of the recent advances in SHM applied to composite structures; also, most of the studies reported in the literature seem to have concentrated on the flat composite plates and reinforced with synthetic fibre. There are relatively fewer stories on other structural configurations such as single or double curve structures and hybridised composites reinforced with natural and synthetic fibres as regards SHM
Solar Power System Plaing & Design
Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies
NASA Tech Briefs, June 2001
Topics covered include: Sensors; Electronic Components and Systems; Software Engineering; Materials; Manufacturing/Fabrication; physical Sciences; Information Sciences
Biomedical Signal and Image Processing
Written for senior-level and first year graduate students in biomedical signal and image processing, this book describes fundamental signal and image processing techniques that are used to process biomedical information. The book also discusses application of these techniques in the processing of some of the main biomedical signals and images, such as EEG, ECG, MRI, and CT. New features of this edition include the technical updating of each chapter along with the addition of many more examples, the majority of which are MATLAB based
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