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You searched for subject:(Flow regime identification). Showing records 1 – 3 of 3 total matches.

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1. Kurwitz, Richard C. PROBABILISTIC PREDICTION USING EMBEDDED RANDOM PROJECTIONS OF HIGH DIMENSIONAL DATA.

Degree: 2011, Texas A&M University

The explosive growth of digital data collection and processing demands a new approach to the historical engineering methods of data correlation and model creation. A new prediction methodology based on high dimensional data has been developed. Since most high dimensional data resides on a low dimensional manifold, the new prediction methodology is one of dimensional reduction with embedding into a diffusion space that allows optimal distribution along the manifold. The resulting data manifold space is then used to produce a probability density function which uses spatial weighting to influence predictions i.e. data nearer the query have greater importance than data further away. The methodology also allows data of differing phenomenology e.g. color, shape, temperature, etc to be handled by regression or clustering classification. The new methodology is first developed, validated, then applied to common engineering situations, such as critical heat flux prediction and shuttle pitch angle determination. A number of illustrative examples are given with a significant focus placed on the objective identification of two-phase flow regimes. It is shown that the new methodology is robust through accurate predictions with even a small number of data points in the diffusion space as well as flexible in the ability to handle a wide range of engineering problems. Advisors/Committee Members: Best, Frederick R. (advisor), Peddicord, Kenneth L. (committee member), O'Neal, Dennis L. (committee member), Hassan, Yassin A. (committee member).

Subjects/Keywords: Dimension Reduction; Manifold Learning; Flow Regime Identification; Random Projections

…122 FLOW REGIME IDENTIFICATION… …91 Flow Regime Imaging… …135 Figure 6.6 Montage of Frame Captures from Flow Regime Video Database.............. 137… …6.12 Plot of the First Two Embeding Coordinates for Flow Regime Video Data… …145 Figure 6.16 Flow Regime Map of Clustered Data… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kurwitz, R. C. (2011). PROBABILISTIC PREDICTION USING EMBEDDED RANDOM PROJECTIONS OF HIGH DIMENSIONAL DATA. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2009-05-519

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Kurwitz, Richard C. “PROBABILISTIC PREDICTION USING EMBEDDED RANDOM PROJECTIONS OF HIGH DIMENSIONAL DATA.” 2011. Thesis, Texas A&M University. Accessed January 18, 2020. http://hdl.handle.net/1969.1/ETD-TAMU-2009-05-519.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kurwitz, Richard C. “PROBABILISTIC PREDICTION USING EMBEDDED RANDOM PROJECTIONS OF HIGH DIMENSIONAL DATA.” 2011. Web. 18 Jan 2020.

Vancouver:

Kurwitz RC. PROBABILISTIC PREDICTION USING EMBEDDED RANDOM PROJECTIONS OF HIGH DIMENSIONAL DATA. [Internet] [Thesis]. Texas A&M University; 2011. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-05-519.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kurwitz RC. PROBABILISTIC PREDICTION USING EMBEDDED RANDOM PROJECTIONS OF HIGH DIMENSIONAL DATA. [Thesis]. Texas A&M University; 2011. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-05-519

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Georgia Tech

2. Xie, Tao. Hydrodynamic characteristics of gas/liquid/fiber three-phase flows based on objective and minimally-intrusive pressure fluctuation measurements.

Degree: PhD, Mechanical Engineering, 2004, Georgia Tech

Flow regime identification in industrial systems that rely on complex multi-phase flows is crucial for their safety, control, diagnostics, and operation. The objective of this investigation was to develop and demonstrate objective and minimally-intrusive flow regime classification methods for gas/water/paper pulp three-phase slurries, based on artificial neural network-assisted recognition of patterns in the statistical characteristics of pressure fluctuations. Experiments were performed in an instrumented three-phase bubble column featuring vertical, upward flow. The hydrodynamics of low consistency (LC) gas-liquid-fiber mixtures, over a wide range of superficial phase velocities, were investigated. Flow regimes were identified, gas holdup (void fraction) was measured, and near-wall pressure fluctuations were recorded using high-sensitivity pressure sensors. Artificial neural networks of various configurations were designed, trained and tested for the classification of flow regimes based on the recorded pressure fluctuation statistics. The feasibility of flow regime identification based on statistical properties of signals recorded by a single sensor was thereby demonstrated. The transportability of the developed method, whereby an artificial neural network trained and tested with a set of data is manipulated and used for the characterization of an unseen and different but plausibly similar data set, was also examined. An artificial neural network-based method was developed that used the power spectral characteristics of the normal pressure fluctuations as input, and its transportability between separate but in principle similar sensors was successfully demonstrated. An artificial neural network-based method was furthermore developed that enhances the transportability of the aforementioned artificial neural networks that were trained for flow pattern recognition. While a redundant system with multiple sensors is an obvious target application, such robustness of algorithms that provides transportability will also contribute to performance with a single sensor, shielding effects of calibration changes or sensor replacements. Advisors/Committee Members: S. Mostafa Ghiaasiaan (Committee Chair), Andrei G. Fedorov (Committee Member), D. William Tedder (Committee Member), Minami Yoda (Committee Member), Seppo Karrila (Committee Member), Tom McDonough (Committee Member).

Subjects/Keywords: Fibers; Flow regime identification; Three phase flow; Artificial neural networks; Pressure fluctuation; Power spectral density; Multiphase flow; Neural networks (Computer science); Neural networks (Computer science); Multiphase flow; Hydrodynamics; Fluid dynamics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Xie, T. (2004). Hydrodynamic characteristics of gas/liquid/fiber three-phase flows based on objective and minimally-intrusive pressure fluctuation measurements. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/4814

Chicago Manual of Style (16th Edition):

Xie, Tao. “Hydrodynamic characteristics of gas/liquid/fiber three-phase flows based on objective and minimally-intrusive pressure fluctuation measurements.” 2004. Doctoral Dissertation, Georgia Tech. Accessed January 18, 2020. http://hdl.handle.net/1853/4814.

MLA Handbook (7th Edition):

Xie, Tao. “Hydrodynamic characteristics of gas/liquid/fiber three-phase flows based on objective and minimally-intrusive pressure fluctuation measurements.” 2004. Web. 18 Jan 2020.

Vancouver:

Xie T. Hydrodynamic characteristics of gas/liquid/fiber three-phase flows based on objective and minimally-intrusive pressure fluctuation measurements. [Internet] [Doctoral dissertation]. Georgia Tech; 2004. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/1853/4814.

Council of Science Editors:

Xie T. Hydrodynamic characteristics of gas/liquid/fiber three-phase flows based on objective and minimally-intrusive pressure fluctuation measurements. [Doctoral Dissertation]. Georgia Tech; 2004. Available from: http://hdl.handle.net/1853/4814

3. Lacayo Ortiz, Juan Manuel. Pressure Normalization of Production Rates Improves Forecasting Results.

Degree: 2013, Texas A&M University

New decline curve models have been developed to overcome the boundary-dominated flow assumption of the basic Arps? models, which restricts their application in ultra-low permeability reservoirs exhibiting long-duration transient flow regimes. However, these new decline curve analysis (DCA) methods are still based only on production rate data, relying on the assumption of stable flowing pressure. Since this stabilized state is not reached rapidly in most cases, the applicability of these methods and the reliability of their solutions may be compromised. In addition, production performance predictions cannot be disassociated from the existing operation constraints under which production history was developed. On the other hand, DCA is often carried out without a proper identification of flow regimes. The arbitrary application of DCA models regardless of existing flow regimes may produce unrealistic production forecasts, because these models have been designed assuming specific flow regimes. The main purpose of this study was to evaluate the possible benefits provided by including flowing pressures in production decline analysis. As a result, it have been demonstrated that decline curve analysis based on pressure-normalized rates can be used as a reliable production forecasting technique suited to interpret unconventional wells in specific situations such as unstable operating conditions, limited availability of production data (short production history) and high-pressure, rate-restricted wells. In addition, pressure-normalized DCA techniques proved to have the special ability of dissociating the estimation of future production performance from the existing operation constraints under which production history was developed. On the other hand, it was also observed than more consistent and representative flow regime interpretations may be obtained as diagnostic plots are improved by including MBT, pseudovariables (for gas wells) and pressure-normalized rates. This means that misinterpretations may occur if diagnostic plots are not applied correctly. In general, an improved forecasting ability implies greater accuracy in the production performance forecasts and more reliable reserve estimations. The petroleum industry may become more confident in reserves estimates, which are the basis for the design of development plans, investment decisions, and valuation of companies? assets. Advisors/Committee Members: Lee, John (advisor), McVay, Duane (committee member), Barrufet, Maria (committee member).

Subjects/Keywords: Pressure-normalized decline curve analysis; Pressure normalized decline curve analysis; Flow regime identification; Pressure normalization of production rates; boundary-dominated flow; boundary dominated flow; transient flow regime; unconventional reservoirs; rate transient analysis; shale gas; shale oil; production forecasting; reserves estimation; hindcasting; hindcasts; low permeability; tight sands; high pressure rate controlled; unstable flowing conditions.

…66 4.3.4. Flow Regime Identification… …72 5.1.2. Flow Regime Identification… …102 Fig. 48—Identification of linear flow regime for well EF-2. Top: Log q - Log t; middle… …Table 4— Log-Log plots for flow regime identification… …70 Table 8— Diagnostic plots for flow regime identification… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lacayo Ortiz, J. M. (2013). Pressure Normalization of Production Rates Improves Forecasting Results. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/151370

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lacayo Ortiz, Juan Manuel. “Pressure Normalization of Production Rates Improves Forecasting Results.” 2013. Thesis, Texas A&M University. Accessed January 18, 2020. http://hdl.handle.net/1969.1/151370.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lacayo Ortiz, Juan Manuel. “Pressure Normalization of Production Rates Improves Forecasting Results.” 2013. Web. 18 Jan 2020.

Vancouver:

Lacayo Ortiz JM. Pressure Normalization of Production Rates Improves Forecasting Results. [Internet] [Thesis]. Texas A&M University; 2013. [cited 2020 Jan 18]. Available from: http://hdl.handle.net/1969.1/151370.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lacayo Ortiz JM. Pressure Normalization of Production Rates Improves Forecasting Results. [Thesis]. Texas A&M University; 2013. Available from: http://hdl.handle.net/1969.1/151370

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.