Gotowe bibliografie tematyczne / Network Forming Liquids

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Gotowa bibliografia na temat „Network Forming Liquids”

Autor: Grafiati

Data publikacji: 7 września 2023

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Artykuły w czasopismach na temat "Network Forming Liquids"

Sasaki, Takashi, Yuya Tsuzuki i Tatsuki Nakane. "A Dynamically Correlated Network Model for the Collective Dynamics in Glass-Forming Molecular Liquids and Polymers". Polymers 13, nr 19 (6.10.2021): 3424. http://dx.doi.org/10.3390/polym13193424.

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The non-Arrhenius behavior of segmental dynamics in glass-forming liquids is one of the most profound mysteries in soft matter physics. In this article, we propose a dynamically correlated network (DCN) model to understand the growing behavior of dynamically correlated regions during cooling, which leads to the viscous slowdown of supercooled liquids. The fundamental concept of the model is that the cooperative region of collective motions has a network structure that consists of string-like parts, and networks of various sizes interpenetrate each other. Each segment undergoes dynamical coupling with its neighboring segments via a finite binding energy. Monte Carlo simulations showed that the fractal dimension of the DCNs generated at different temperatures increased and their size distribution became broader with decreasing temperature. The segmental relaxation time was evaluated based on a power law with four different exponents for the activation energy of rearrangement with respect to the DCN size. The results of the present DCN model are consistent with the experimental results for various materials of molecular and polymeric liquids.

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Takéuchi, Yasushi. "Hydrodynamic Scaling and the Intermediate-Range Order in Network-Forming Liquids". Progress of Theoretical Physics Supplement 178 (2009): 181–86. http://dx.doi.org/10.1143/ptps.178.181.

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Hong, N. V., N. V. Huy i P. K. Hung. "The structure and dynamic in network forming liquids: molecular dynamic simulation". International Journal of Computational Materials Science and Surface Engineering 5, nr 1 (2012): 55. http://dx.doi.org/10.1504/ijcmsse.2012.049058.

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Yang, Ke, Zhikun Cai, Madhusudan Tyagi, Mikhail Feygenson, Joerg C. Neuefeind, Jeffrey S. Moore i Yang Zhang. "Odd–Even Structural Sensitivity on Dynamics in Network-Forming Ionic Liquids". Chemistry of Materials 28, nr 9 (25.04.2016): 3227–33. http://dx.doi.org/10.1021/acs.chemmater.6b01429.

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Galimzyanov, Bulat N., Maria A. Doronina i Anatolii V. Mokshin. "Arrhenius Crossover Temperature of Glass-Forming Liquids Predicted by an Artificial Neural Network". Materials 16, nr 3 (28.01.2023): 1127. http://dx.doi.org/10.3390/ma16031127.

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The Arrhenius crossover temperature, TA, corresponds to a thermodynamic state wherein the atomistic dynamics of a liquid becomes heterogeneous and cooperative; and the activation barrier of diffusion dynamics becomes temperature-dependent at temperatures below TA. The theoretical estimation of this temperature is difficult for some types of materials, especially silicates and borates. In these materials, self-diffusion as a function of the temperature T is reproduced by the Arrhenius law, where the activation barrier practically independent on the temperature T. The purpose of the present work was to establish the relationship between the Arrhenius crossover temperature TA and the physical properties of liquids directly related to their glass-forming ability. Using a machine learning model, the crossover temperature TA was calculated for silicates, borates, organic compounds and metal melts of various compositions. The empirical values of the glass transition temperature Tg, the melting temperature Tm, the ratio of these temperatures Tg/Tm and the fragility index m were applied as input parameters. It has been established that the temperatures Tg and Tm are significant parameters, whereas their ratio Tg/Tm and the fragility index m do not correlate much with the temperature TA. An important result of the present work is the analytical equation relating the temperatures Tg, Tm and TA, and that, from the algebraic point of view, is the equation for a second-order curved surface. It was shown that this equation allows one to correctly estimate the temperature TA for a large class of materials, regardless of their compositions and glass-forming abilities.

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Liu, Mengtan, Ryan D. McGillicuddy, Hung Vuong, Songsheng Tao, Adam H. Slavney, Miguel I. Gonzalez, Simon J. L. Billinge i Jarad A. Mason. "Network-Forming Liquids from Metal–Bis(acetamide) Frameworks with Low Melting Temperatures". Journal of the American Chemical Society 143, nr 7 (11.02.2021): 2801–11. http://dx.doi.org/10.1021/jacs.0c11718.

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Zhu, W., Y. Xia, B. G. Aitken i S. Sen. "Temperature dependent onset of shear thinning in supercooled glass-forming network liquids". Journal of Chemical Physics 154, nr 9 (7.03.2021): 094507. http://dx.doi.org/10.1063/5.0039798.

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Hong, N. V., N. V. Huy i P. K. Hung. "The correlation between coordination and bond angle distribution in network-forming liquids". Materials Science-Poland 30, nr 2 (czerwiec 2012): 121–30. http://dx.doi.org/10.2478/s13536-012-0019-y.

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Maruyama, Kenji, Hirohisa Endo i Hideoki Hoshino. "Voids and Intermediate-Range Order in Network-Forming Liquids: Rb20Se80 and BiBr3". Journal of the Physical Society of Japan 76, nr 7 (15.07.2007): 074601. http://dx.doi.org/10.1143/jpsj.76.074601.

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Hung, P. K., P. H. Kien, L. T. San i N. V. Hong. "The study of diffusion in network-forming liquids under pressure and temperature". Physica B: Condensed Matter 501 (listopad 2016): 18–25. http://dx.doi.org/10.1016/j.physb.2016.07.033.

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Rozprawy doktorskie na temat "Network Forming Liquids"

Sharma, Ruchi. "Computational studies of network-forming liquids: multiple time-scale behavior and water-like anomalies". Thesis, 2009. http://localhost:8080/iit/handle/2074/3690.

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Witman, Jennifer Elisabeth. "The T-Shaped Anisotropic Molecule Model: A Unique Perspective on the Glass Transition and Gelation in Low Valence, Directional, Network Forming Liquids". Thesis, 2010. https://thesis.library.caltech.edu/5715/4/04-_Appendices.pdf.

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Glass and gel formers exhibit unusual mechanical characteristics and amorphous phases which are highly dependent on their thermal history. We introduce a lattice model with T-shaped molecules that exhibits glassy and gel-like states without introducing artificial frustration. This system has a large number of degenerate energy minima separated by small barriers leading to a broad, kinetically-explored landscape. It particularly replicates valence-limited materials, which can form self-assembled materials with highly controlled physical properties. Despite its remarkable simplicity, this model reveals some of the fundamental kinetic and thermodynamic properties of the glass transition and of gel formation.

A dearth of low temperature experimental and simulation measurements has inhibited investigation in this field. We overcome this difficulty by using a modified Metropolis Monte Carlo method to quickly provide equilibrium samples. Then kinetic Monte Carlo techniques are used to explore the properties of the equilibrium system, providing a touchstone for the non-equilibrium glassy states.

Fully-dense simulation samples reveal a fragile-to-strong crossover (FSC) near the mean-field (MF) spinodal. At the FSC, the relaxation time returns to Arrhenius behavior with cooling. There is an inflection point in the configurational entropy. This behavior resolves the Kauzmann Paradox which is a result of extrapolation from above the inflection point. In contrast, we find that the configurational entropy remains finite as the temperature goes to zero. We also observe different kinetics as the system is quenched below the FSC, resulting in non-equilibrium, amorphous states with high potential energy persisting for long periods of time. Simulation samples remain at non-equilibrium conditions for observation times exceeding those permitting complete equilibration slightly above the FSC. This suggests the FSC would often be identified as the glass transition without indication that there is true arrest or a diverging length scale. Indeed, our simulations show these samples do equilibrate if sufficient time is allowed. To elucidate the complex, interdependent relation time and length scales at the FSC will require careful consideration of the spatial-dynamic heterogeneity.

Dynamic mean-field simulations at high density and in the solvated regime reveal a rich range of morphological features. They are consistent with simulated and experimental results in colloidal systems. Stability limits of decreasing length scales beneath the phase separation bimodal coincide into a single curve, which terminates at the fully-dense MF spinodal, suggesting that gelation and vitrification are the same phenomena. Our work indicates that gelation is, therefore, a result of phase separation arrested by a glass transition.

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Agarwal, Manish. "Structure, entropy and transport in network-forming liquid". Thesis, 2010. http://localhost:8080/iit/handle/2074/3693.

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Części książek na temat "Network Forming Liquids"

Marcus, Yizhak. "Network Forming Ionic Liquids". W Ionic Liquid Properties, 99–107. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30313-0_4.

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Yarger, J. L., C. A. Angell, S. S. Borick i G. H. Wolf. "Polyamorphic Transitions in Network-Forming Liquids and Glasses". W ACS Symposium Series, 214–23. Washington, DC: American Chemical Society, 1997. http://dx.doi.org/10.1021/bk-1997-0676.ch016.

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Bakó, I., P. Jedlovszky, G. Pálinkás i J. C. Dore. "Investigation of the Structure of Liquid Formic Acid". W Hydrogen Bond Networks, 119–27. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-015-8332-9_13.

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Güneş, Hasan Veysi. "Hücre Biyolojisi". W Moleküler Biyoloji ve Genetik, 1–36. Türkiye Bilimler Akademisi, 2023. http://dx.doi.org/10.53478/tuba.978-625-8352-48-1.ch01.

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The cell was first described by Robert Hooke in 1665. The living world is divided into two groups according to the presence or absence of a nuclear membrane in its cells. Prokaryotic organisms do not have a nuclear membrane whereas eukaryotic organisms have a nuclear membrane. When we examine it structurally, the cell consists of cell membrane, cytoplasm and nucleus. The cell membrane is made of a double lipit layer and proteins located on the lipit surface and embedded in the lipit layer. Cell membrane determines the boundaries of the cell and performs tasks such as forming the cell shape, controlling the entry and exit of various substances into the cell, and receiving various warning signals reaching the cell. The cytoplasm consists of a colloid liquid part and organelles. In the cytosol, there are Microtubules, Intermediate filaments and Microfilaments (actin filaments), which are cytoskeletal elements. In addition to these, there are Endoplasmic reticulum, Ribosome, Golgi complex, Lysosome, Peroxisome, Mitochondria and Centrosome as organelles in the cytoplasm. The nucleus, usually located in the middle of the cell, is surrounded by a double membrane. The outer membrane is associated with the rough endoplasmic reticulum located in the cytoplasm. There are channels on the outer membrane called porin. These two membrane units, also called the nuclear envelope, disperse in late prophase of cell division and remodel around new chromosomes in telophase. The core envelope is surrounded on both sides by intermediate filaments. While the filaments surrounding the outer membrane are less organized, the filaments surrounding the inner membrane in the form of a fibrous network, are organized quite regularly and this fibrous network-shaped structure is called the nuclear lamina. An undivided, interphase nucleus contains colloidal plasma forming the liquid part, chromatin threads and nucleolus.

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Streszczenia konferencji na temat "Network Forming Liquids"

Viola, Ilenia, Roberto Cingolani i Giuseppe Gigli. "A Micro-Fluidic Real-Time Monitoring of the Dynamics of Polymeric Liquids". W ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58316.

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Manipulating liquid fluids within networks of micro-channels is crucial in the fabrication of micro-fluidic devices for electronic and for medical applications, as well as for the fundamental understanding of the fluid dynamics properties in geometrical confined systems, in which localization phenomena and surface effects can strongly affect the fluid behaviour. A detailed analysis of the fluid dynamics properties is particularly important for glass-forming liquids, polymeric solutions or bio-fluids, which do not behave as Newtonian fluids, due to the the elasticity of the molecules. For these materials the determination of the structural dynamical parameters is not trivial, requiring the solution of a complex many body problem and the introduction of non-linear mechanical parameters. In this work we have provided a micro-fluidic approach to assess the structural rheological parameters of a glass-forming liquid under real operation conditions. The method was applied to a viscoelastic polymeric liquid, polyurethane (PU) adhesive, during its driving flow in a micro-capillary network.

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Takéuchi, Yasushi. "Can Molecular Dynamics Simulations Trace the Long-Time Relaxations in Network-Glass-Forming Liquids?" W Proceedings of the 12th Asia Pacific Physics Conference (APPC12). Journal of the Physical Society of Japan, 2014. http://dx.doi.org/10.7566/jpscp.1.016007.

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Ocalan, Murat, John P. Edlebeck i Shane P. Siebenaler. "Acoustic Leak Detection at a Distance: A Key Enabler for Real-Time Pipeline Monitoring With the Internet of Things". W 2016 11th International Pipeline Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/ipc2016-64405.

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Real-time leak monitoring of pipelines is a need that is growing with the aging of the assets and the rise of the population living in their close proximity. While traditional deployment of external monitoring solutions on legacy assets may require extensive construction and trenching on the pipeline right-of-way, a new class of self-powered and wirelessly communicating devices provides an intriguing alternative. These devices are installed on the right-of-way with no need for mechanical excavation and allow continuous monitoring of a pipeline over long distances. Their low-power requirement makes it possible to operate the monitoring system continuously on battery power and their wireless communication is established through a self-forming network. These attributes make real-time monitoring possible without requiring any wiring to be deployed on the right-of way. The devices take advantage of the pipe’s characteristics that guide the acoustic waves generated by the leak along the pipeline to detect leaks. These characteristics make the detection possible even from a device that is not in close proximity of the leak. Since device spacing is a key parameter in the cost of monitoring with the leak detection system, it is important to understand the parameters that govern the propagation of leak sound on pipelines. Testing was performed for this purpose to validate the ability of these novel acoustic sensors in an outdoor test facility under a variety of leak conditions. This testing evaluated the propagation of acoustic waves emanating from small leaks on a buried pipe. This was achieved by pressurizing the pipeline to different levels of pressure and inducing leaks through various orifice sizes. The acoustic disturbances induced by these leaks were measured by sensors deployed at various stations on the pipe. The results of this testing demonstrated the ability of such an approach to be used for detecting very small disturbances in soil from an offset position caused by leaking liquids.

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Popelka, Anton, Salma Habib, Aya Abusrafa, Fathima Sifani Zavahir i Asma Abdulkareem. "Preparation of Slippery Liquid Infused Porous Surfaces on Polymeric Substrates". W Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2020. http://dx.doi.org/10.29117/quarfe.2020.0012.

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Many polymers have been found in bioscience paralleling with advancement in a technology sector. A selection of suitable polymers for using in a biomedical sector is based on many factors such as chemical nature, surface free energy or morphology, which influence cell-polymer surface interactions. However, these materials suffering from infections represent serious issues for their applications. These infections closely relate with biofilm formation, whereby microorganisms are strongly attached to surface forming strong attached multicellular communities. Therefore, a preparation of slippery liquid infused porous surfaces (SLIPS) using low-temperature plasma technique in combination with electrospinning technique was utilized in this research. A multistep physicochemical approach was carried out for this purpose. The first step includes the pretreatment of polyethylene (PE) and polyurethane (PU) substrates using low-temperature plasma to activate the surface for an adhesion improvement. Subsequently, the 3D porous network consisted of superhydrophobic fiber mats, that was fabricated on the plasma activated substrates using electrospinning technique. Final step consisted of the infusion of natural oils with emphasis on their antimicrobial effect. This complex strategy led to the effective antimicrobial modification of the PE and PU surface potentially applicable in the biomedical field.

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Schiaffino, Arturo, V. M. Krushnarao Kotteda, Arturo Bronson, Sanjay Shantha-Kumar i Vinod Kumar. "Predicting the Depth of Penetration of Molten Metal Into a Pore Network Using TensorFlow". W ASME 2018 5th Joint US-European Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/fedsm2018-83258.

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Liquid metal infiltration consists of infusing liquid metal into a porous media or a packed bed of boron carbide powder to react and create ultimately a metal or ceramic matrix embedded with boride-carbide precipitates. The purpose of the study is to model the liquid flow into the capillaries of the packed bed by using machine learning algorithms from an open source available as TensorFlow library created by Google Brain. The library has a variety of algorithms including training and inference algorithms forming deep neural network models to predict the wetting dynamics, flow resistance, and the depth/rate of penetration into the capillaries of the packed bed. In the present work, the results from the machine-learning python code based on the TensorFlow library is compared against the experimental data obtained for molten Hf-Ti-Y-Zr alloys infiltrating into a packed bed of boron carbide at temperatures up to 2300°C. A summary of the techniques used to tweak the machine learning algorithms to predict the infusion behavior will be presented.

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Aguilar, Julio, Laura Sandoval, Arturo Rodriguez, Sanjay Shantha Kumar, Jose Terrazas, Richard Adansi, Vinod Kumar i Arturo Bronson. "A CNN With Deep Learning for Non-Equilibrium Characterization of Al-Sm Melt Infusion Into a B4C Packed Bed". W ASME 2021 Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/fedsm2021-65794.

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Abstract In seeking predictability of characterizing materials for ultra-high temperature materials for hypersonic vehicles, the use of the convolutional neural network for characterizing the behavior of liquid Al-Sm-X (Hf, Zr, Ti) alloys within a B4C packed to determine the reaction products for which they are usually done with the scanning electron microscope (SEM) or X-ray diffraction (XRD) at ultra-high temperatures (> 1600°C). Our goal is to predict ultimately the products as liquid Al-Sm-X (Hf, Zr, Ti) alloys infiltrate into a B4C packed bed. Material characterization determines the processing path and final species from the reacting infusion consisting of fluid flow through porous channels, consumption of elemental components, and reaction forming boride and carbide precipitates. Since characterization is time-consuming, an expert in this field is required; our approach is to characterize and track these species using a Convolutional Neural Network (CNN) to facilitate and automate analysis of images. Although Deep Learning seems to provide an automated prediction approach, some of these challenges faced under this research are difficult to overcome. These challenges include data required, accuracy, training time, and computational cost requirements for a CNN. Our approach was to perform experiments on high-temperature metal infusion under B4C Packed Bed infiltration in a parametric matrix of cases. We characterized images using SEM and XRD images and run/optimize our CNN, which yields an innovative method for characterization via Deep Learning compared to traditional practices.

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Martinez Lucci, Jose, R. S. Amano i Pradeep Rohatgi. "Computational Analysis of Self-Healing in a Polymer Matrix With Microvascular Networks". W ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-50148.

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For the last decade, many researchers have been working to develop self-healing materials, and have obtained good results in the field of polymers, these components with microencapsulated healing agent have exhibited noticeable mechanical performance and regenerative property The research described in this paper applies the concept of self healing to simulate self healing polymer matrix composites, with the aid of models developed by the authors for the manufacturing processes and self-healing behavior. The development of self-healing is a novel idea that has not been totally explored in great detail yet. The concept of self-healing described in this paper consists of simulation of a healing agent dicyclopentadiene (DCPD) inside of a microvascular network within a polymer matrix coating with catalyst forming a self-healing composite (SHC). When this SHC is damaged or cracked, the healing agent by capillary action will flow inside of the microvascular network; when the liquid enter in contact with the catalyst will form a polymer structure and sealing the crack. The study consists of theoretical analysis and Computational Fluid Dynamics of a self-healing polymer. The objective of the study reported here was to find the influence and efficiency of the microvascular network in healing a polymer matrix. To check this effect a computational model was created to simulate the healing treatment, thus a crack was created on the matrix surface piercing the microvascular network filled with healing agent and the method to simulate healing behavior of the composite allows assessment of the effects of the autonomously repairing repeated damage events.

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Murakami, Daisuke, i Kenji Yasuoka. "Molecular Dynamics Simulation of Quasi-Two-Dimensional Water Network on Ice Nucleation Protein". W ASME/JSME 2011 8th Thermal Engineering Joint Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/ajtec2011-44609.

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An ice nucleation protein induces a phase transition from liquid water to ice in air. A specific hydrophilic surface of the protein may have an influence on the network of hydrogen bonds touching on the protein. However, microscopic characteristics of the ice nucleation protein and behavior of water molecules on it have not been clarified. So we carried out molecular dynamics simulations in various quasi-two-dimensional densities of water molecules on the ice nucleation protein. The percolation threshold of water clusters was confirmed. Comparing another hydrophilic protein, the threshold density in both cases had nearly the same value. But percolation probabilities and mean cluster sizes near the threshold were different between both cases. Those results implied that the threshold density was consistent with the conventional theory, but the forming of water clusters near the threshold was influenced by the hydrophilicity on the ice nucleation protein.

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Shaik, Mohammed Riyazuddin. "Pipeline Integrity Assessment: Methodology". W ASME 2015 India International Oil and Gas Pipeline Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/iogpc2015-7904.

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With ever increasing energy demands, approximately 90 million barrels of oil per day and 3314 billion cubic meters of gas per year are consumed around the world [1]. To meet such huge energy demands a complicated and vast network of offshore and onshore production and distribution pipelines is necessary. Pipelines connect areas that are relatively rich in resources with areas that are demand-hungry but poor in resources. They play a central role in providing to the energy needs of businesses and public, forming the veins and arteries of the Oil and Gas industry. These pipelines are susceptible to damage, both internal and external based on the type of product in the pipeline and the environment in the vicinity of the pipeline i.e. offshore or onshore. The damage to the pipeline needs to be identified and the significance of the damage clearly defined. The inline inspection (ILI) tools help to identify the damage and record the extent and type of damage. Inability to prioritize the damaged areas and carry out necessary intervention to at least curtail the damage may occasionally lead to calamitous consequences. One such example is of a 30-inch Liquid pipeline failure that occurred in Michigan on July 25, 2010. The National Transportation Safety Board (NTSB) reported that the corrosion fatigue cracks that grew and coalesced from corrosion defects resulted in the rupture and prolonged release from the 30-inch oil pipeline [2]. This failure resulted in a revenue loss of approximately $16 million and estimated costs of $767 million for regulatory and professional support in connection with the clean-up operations. Condition assessment of the pipelines as part of the pipeline integrity management system is the primary means through which such catastrophic pipeline system failures could be prevented. This paper presents the methodology that is adopted for “Integrity Assessment” of the pipelines.

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Bahrami, Peyman, i Lesley A. James. "Field Production Optimization Using Smart Proxy Modeling; Implementation of Sequential Sampling, Average Feature Ranking, and Convolutional Neural Network". W SPE Canadian Energy Technology Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212809-ms.

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Abstract This work aims to create an approximation of the reservoir numerical model using smart proxy modeling (SPM) to be used for production optimization. The constructed SPM in this work is further improved in different steps to increase its accuracy and efficiency compared to the existing literature. These steps include sequential sampling, average feature ranking, convolutional neural network (CNN) deep learning modeling, and feature engineering. SPM is a novel methodology that generates results faster than numerical simulators. SPM decouples the mathematical equations of the problem into a numeric dataset and trains a statistical/AI-driven model on the dataset. Major SPM construction steps are: objective, input, and output selection, sampling, running numerical model, extracting new static and dynamic parameters, forming a new dataset, performing feature selection, training and validating the underlying model, and employing the SPM. Unlike traditional proxy modeling, SPM implements feature engineering techniques that generate new static/dynamic parameters. The extracted parameters help to capture hidden patterns within the dataset, eventually increasing SPMs’ accuracy. SPM can either be constructed to predict the grids’ characteristics, called grid-based SPM, or to predict the wells' fluid rates, called well-based SPM. In this work, the well-based SPM is constructed to duplicate the Volve offshore field production results undergoing waterflooding. We used Latin hypercube sampling coupled with genetic algorithm (GA) in the sampling step. The designed parameters to perform sampling are the individual liquid rate of the producers, and the output is the individual well's cumulative oil production. In the formed dataset, various extracted parameters relating to the wells are prepared, such as well types, indexes, trajectories, and cumulative oil production. Furthermore, a grid-based SPM is constructed in parallel to the well-based SPM. At each timestep of the prediction, dynamic parameters relating to grids (in this case: grids’ pressure/saturations) are transferred to the existing well-based dataset. This technique helps the well-based SPM further increase in accuracy by finding new patterns within the dataset. We implement an average of 23 different models to rank, and perform the feature selection process. Finally, the CNN model is trained on the dataset, and is coupled with two derivative-free optimizers of GA and particle swarm optimizer to maximize the oil production over the selected time period. Sequential sampling used in this work is a novel technique to construct the SPM with the lowest number of numerical model executions. It provides an efficient workflow to perform sampling, thereby saving time instead of repeating the whole SPM construction steps. The average feature ranking implemented in this paper provides the best prioritization of input parameters. It provides a confident ranking for the feature selection step. Finally, the underlying CNN model is compared to the prediction accuracy of the ANN model.

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