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Register a new userFuzzy inference system application for oil-water flow patterns identification
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Added by
Yuyan Wu
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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With the continuous development of the petroleum industry, long-distance transportation of oil and gas has been the norm. Due to gravity differentiation in horizontal wells and highly deviated wells (non-vertical wells), the water phase at the bottom of the pipeline will cause scaling and corrosion in the pipeline. Scaling and corrosion will make the transportation process difficult, and transportation costs will be considerably increased. Therefore, the study of the oil-water two-phase flow pattern is of great importance to oil production. In this paper, a fuzzy inference system is used to predict the flow pattern of the fluid, get the prediction result, and compares it with the prediction result of the BP neural network. From the comparison of the results, we found that the prediction results of the fuzzy inference system are more accurate and reliable than the prediction results of the BP neural network. At the same time, it can realize real-time monitoring and has less error control. Experimental results demonstrate that in the entire production logging process of non-vertical wells, the use of a fuzzy inference system to predict fluid flow patterns can greatly save production costs while ensuring the safe operation of production equipment.
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Causal inference is perhaps one of the most fundamental concepts in science, beginning originally from the works of some of the ancient philosophers, through today, but also weaved strongly in current work from statisticians, machine learning experts, and scientists from many other fields. This paper takes the perspective of information flow, which includes the Nobel prize winning work on Granger-causality, and the recently highly popular transfer entropy, these being probabilistic in nature. Our main contribution will be to develop analysis tools that will allow a geometric interpretation of information flow as a causal inference indicated by positive transfer entropy. We will describe the effective dimensionality of an underlying manifold as projected into the outcome space that summarizes information flow. Therefore contrasting the probabilistic and geometric perspectives, we will introduce a new measure of causal inference based on the fractal correlation dimension conditionally applied to competing explanations of future forecasts, which we will write $GeoC_{yrightarrow x}$. This avoids some of the boundedness issues that we show exist for the transfer entropy, $T_{yrightarrow x}$. We will highlight our discussions with data developed from synthetic models of successively more complex nature: then include the H{e}non map example, and finally a real physiological example relating breathing and heart rate function. Keywords: Causal Inference; Transfer Entropy; Differential Entropy; Correlation Dimension; Pinskers Inequality; Frobenius-Perron operator.
Graph neural network (GNN) is an efficient neural network model for graph data and is widely used in different fields, including wireless communications. Different from other neural network models, GNN can be implemented in a decentralized manner with information exchanges among neighbors, making it a potentially powerful tool for decentralized control in wireless communication systems. The main bottleneck, however, is wireless channel impairments that deteriorate the prediction robustness of GNN. To overcome this obstacle, we analyze and enhance the robustness of the decentralized GNN in different wireless communication systems in this paper. Specifically, using a GNN binary classifier as an example, we first develop a methodology to verify whether the predictions are robust. Then, we analyze the performance of the decentralized GNN binary classifier in both uncoded and coded wireless communication systems. To remedy imperfect wireless transmission and enhance the prediction robustness, we further propose novel retransmission mechanisms for the above two communication systems, respectively. Through simulations on the synthetic graph data, we validate our analysis, verify the effectiveness of the proposed retransmission mechanisms, and provide some insights for practical implementation.
The energy generation of a run of river hydropower plant depends upon the flow of river and the variations in the water flow makes the energy production unreliable. This problem is usually solved by constructing a small pond in front of the run of river hydropower plant. However, changes in water level of conventional single pond model results in sags, surges and unpredictable power fluctuations. This work proposes three pond model instead of traditional single pond model. The volume of water in three ponds is volumetrically equivalent to the traditional single pond but it reduces the dependency of the run of river power plant on the flow of river. Moreover, three pond model absorbs the water surges and disturbances more efficiently. The three pond system, modeled as non-linear hydraulic three tank system, is being applied with fuzzy inference system and standard PID based methods for smooth and efficient level regulation. The results of fuzzy inference system are across-the-board improved in terms of regulation and disturbances handling as compared to conventional PID controller.
Microfluidic techniques have been extensively developed to realize micro-total analysis systems in a small chip. For microanalysis, electro-magnetic forces have generally been utilized for the trapping of objects, but hydrodynamics has been little explored despite its relevance to pattern formation. Here, we report that water-in-oil (W/O) droplets can be transported in the grid of an array of other large W/O droplets. As each droplet approaches an interspace of the large droplet array, while exhibiting persistent back-and-forth motion, it is conveyed at a velocity equal to the droplet array. We confirm the appearance of closed streamlines in a numerical simulation, suggesting that a vortex-like stream is involved in trapping the droplet. Furthermore, more than one droplet is also conveyed as an ordered cluster with dynamic reposition.
We consider the problem of spatiotemporal sampling in a discrete infinite dimensional spatially invariant evolutionary process $x^{(n)}=A^nx$ to recover an unknown convolution operator $A$ given by a filter $a in ell^1(mathbb{Z})$ and an unknown initial state $x$ modeled as avector in $ell^2(mathbb{Z})$. Traditionally, under appropriate hypotheses, any $x$ can be recovered from its samples on $mathbb{Z}$ and $A$ can be recovered by the classical techniques of deconvolution. In this paper, we will exploit the spatiotemporal correlation and propose a new spatiotemporal sampling scheme to recover $A$ and $x$ that allows to sample the evolving states $x,Ax, cdots, A^{N-1}x$ on a sub-lattice of $mathbb{Z}$, and thus achieve the spatiotemporal trade off. The spatiotemporal trade off is motivated by several industrial applications cite{Lv09}. Specifically, we show that ${x(mmathbb{Z}), Ax(mmathbb{Z}), cdots, A^{N-1}x(mmathbb{Z}): N geq 2m}$ contains enough information to recover a typical low pass filter $a$ and $x$ almost surely, in which we generalize the idea of the finite dimensional case in cite{AK14}. In particular, we provide an algorithm based on a generalized Prony method for the case when both $a$ and $x$ are of finite impulse response and an upper bound of their support is known. We also perform the perturbation analysis based on the spectral properties of the operator $A$ and initial state $x$, and verify them by several numerical experiments. Finally, we provide several other numerical methods to stabilize the method and numerical example shows the improvement.
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