CLASSIFICATION OF MATHEMATICAL MODELS FOR FOREST AND STEPPE FIRES

AbstractIn many oil reservoirs worldwide, the downhole pressure does not have the ability to lift the produced fluids to the surface. In order to produce these fluids, pumps are used to artificially lift the fluids; this method is referred to as artificial lift. More than seventy percent of all currently producing oil wells are being produced by artificial lift methods. One of the most applied artificial lift methods is sucker rod pump. Sucker rod pumps are considered a well-established technology in the oil and gas industry and thus are easy to apply, very common worldwide, and low in capital and operational costs. Many advancements in technology have been applied to improve sucker rod pumps performance, applicability range, and diagnostics. With these advancements, it is important to be able to constantly provide an updated review and guide to the utilization of the sucker rod pumps. This research provides an updated comprehensive review of sucker rod pumps components, diagnostics methods, mathematical models, and common failures experienced in the field and how to prevent and mitigate these failures. Based on the review conducted, a new classification of all the methods that can fall under the sucker rod pump technology based on newly introduced sucker rod pump methods in the industry has been introduced. Several field cases studies from wells worldwide are also discussed in this research to highlight some of the main features of sucker rod pumps. Finally, the advantages and limitations of sucker rod pumps are mentioned based on the updated review. The findings of this study can help increase the understanding of the different sucker rod pumps and provide a holistic view of the beam rod pump and its properties and modeling.

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Classification of Mathematical Models for Process Design

Russian Engineering Research ◽

10.3103/s1068798x21110137 ◽

2021 ◽

Vol 41 (11) ◽

pp. 1045-1048

Author(s):

S. Yu. Kalyakulin ◽

E. V. Mitin ◽

S. P. Sul’din

Keyword(s):

Mathematical Models ◽

Process Design

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Mathematical Modeling of Main Classes of Stochastic Productive Systems

Engineering Technologies and Systems ◽

10.15507/2658-4123.029.201904.496-509 ◽

2019 ◽

Vol 29 (4) ◽

pp. 496-509

Author(s):

Alexander A. Butov ◽

Maxim A. Volkov ◽

Viktor N. Golovanov ◽

Anatoly A. Kovalenko ◽

Boris M. Kostishko ◽

...

Keyword(s):

Mathematical Models ◽

Time Distribution ◽

Just In Time ◽

Birth And Death Processes ◽

Multistage System ◽

Martingale Methods ◽

Time Class ◽

Productive Systems ◽

Time Systems

Introduction. The article deals with mathematical models of two main classes of processes in stochastic productive systems. For a multistage system, conditions of belonging to a “just-in-time” class or a class with infinite support of the time distribution function for productive operations are determined. Materials and Methods. Descriptions and investigations of models are carried out by trajectory (martingale) methods. For “just-in-time” systems and multistage stochastic productive systems, terms and methods of random walks in a random environment and birth and death processes are used. The results are formulated as descriptions of intensity characteristics of equalizers of point counting processes. Results. Two theorems are given and proved; they justify the proposed classification of the mathematical models of productive systems. The criteria of the belonging of the stochastic productive system to the class “just-in-time” are given. A theorem on the incompatibility of groups of “just-in-time” systems and systems infinite support of the time distribution for operations is proved. Discussion and Conclusion. The results show the feasibility of analyzing stochastic productive systems by martingale methods. The descriptions of terms of intensities of the equalizers time of productive processes admit generalization.

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Classification of mathematical models of technological design

10.36652/0042-4633-2021-8-68-71 ◽

2021 ◽

pp. 68-71

Author(s):

Keyword(s):

Mathematical Model ◽

Mathematical Models ◽

Technological Process ◽

Process Design ◽

Geometric Parameters ◽

Technological Design ◽

Technological Processes ◽

Technological Preparation

The use of mathematical models is of great importance for the automation of the design of technological processes. Representation of the geometric parameters of the part in the form of mathematical models allows automating the development of the structure and calculation of the parameters of the technological process, which is important for the complete digitalization of the technological preparation of production. Keywords: technological process, design, mathematical model, digitalization. [email protected]

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Sicegar: R package for sigmoidal and double-sigmoidal curve fitting

10.7287/peerj.preprints.3116v1 ◽

2017 ◽

Author(s):

M. Umut Caglar ◽

Ashley I. Teufel ◽

Claus O Wilke

Keyword(s):

Mathematical Models ◽

Curve Fitting ◽

R Package ◽

Model Fit ◽

Noise Levels ◽

High Noise ◽

Extensive Documentation ◽

Sigmoidal Curve ◽

Sigmoidal Model

Sigmoidal and double-sigmoidal dynamics are commonly observed in many areas of biology. Here we present sicegar, an R package for the automated fitting and classification of sigmoidal and double-sigmodial data. The package categorizes data into one of three categories, "no signal", "sigmodial", or "double sigmoidal", by rigorously fitting a series of mathematical models to the data. The data is labeled as "ambiguous" if neither the sigmoidal nor double-sigmoidal model fit the data well. In addition to performing the classification, the package also reports a wealth of metrics as well as biologically meaningful parameters describing the sigmoidal or double-sigmoidal curves. In extensive simulations, we find that the package performs well, can recover the original dynamics even under fairly high noise levels, and will typically classify curves as "ambiguous" rather than misclassifying them. The package is available on CRAN and comes with extensive documentation and usage examples.

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Mathematical modeling skills development among students of universities of economics

Samara Journal of Science ◽

10.17816/snv202307 ◽

2020 ◽

Vol 9 (2) ◽

pp. 254-257

Author(s):

Sergey I. Makarov

Keyword(s):

Mathematical Modeling ◽

Mathematical Models ◽

Digital Economy ◽

Educational Process ◽

Skills Development ◽

Systems Modeling ◽

Present Stage ◽

Teaching Aids ◽

Electronic Teaching

This paper discusses approaches to economic and mathematical modeling skills development among students of universities of economics. The need for this competency among specialists in the digital economy is shown. The motivation of the student the future specialist in the digital economy in mastering the basic techniques of economic processes and systems modeling is outlined. The sections of the school course in mathematics are given, which are the basis for the development of these skills. Mathematical courses are examined; their study is considered to be the foundation for the development of the competence in economic processes modeling. The author describes the main types of mathematical models that are studied at the present stage at universities of economics and are widely used in the digital economy. The author also presents a classification of the models used in the educational process while studying mathematical courses. The main requirements for economic-mathematical models are discussed and substantiated. The author has listed necessary requirements for teachers of mathematical departments of universities. These requirements can help them to teach basic mathematics and its applied sections (e.g. mathematical modeling) to students successfully. The main conclusions and results of the study can be used in the practical work of teachers of mathematical departments at universities of economics when creating electronic teaching aids of economic and mathematical modeling and methods of their application in the educational process.

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A review of models for investigating the behaviour of nitrogen in soil

Philosophical Transactions of the Royal Society of London Series B Biological Sciences ◽

10.1098/rstb.1982.0010 ◽

1982 ◽

Vol 296 (1082) ◽

pp. 341-349 ◽

Cited By ~ 14

Keyword(s):

Organic Matter ◽

Mathematical Models ◽

Nitrogen Cycle ◽

Statistical Treatment ◽

Scientific Understanding ◽

Data Series ◽

Transport Models ◽

Management Models

An efficient way to integrate knowledge on the behaviour of nitrogen in soil is by the development of mathematical models. Models are developed for several aims, such as prediction, management or scientific understanding. Prediction and management models are mainly based on a statistical treatment of data series from preceding years. Models that have been made to obtain a better understanding of the nitrogen cycle are usually based on a mechanistic description of processes such as leaching, volatilization of ammonia, mineralization, immobilization, nitrification, denitrification and uptake by the roots. Differences between models occur because these processes are described with varying degrees of sophistication; often, the description of one or two of the processes dominate the entire model. Thus, a classification of models can be based on the processes that are emphasized. A useful categorization distinguishes between transport models, organic matter models and soil-plant relation models.

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MATHEMATIC MODELS OF WATER RESOURCES MANAGEMENT FOR IRRIGATION SYSTEMS

TECHNICAL SCIENCES ◽

10.26739/2181-9696-2020-5-10 ◽

2020 ◽

Vol 5 (3) ◽

pp. 64-70

Author(s):

Aysulu Aydarova ◽

Keyword(s):

Water Resources ◽

Mathematical Models ◽

Water Resources Management ◽

Optimal Distribution ◽

Irrigation Systems ◽

New Model ◽

Resources Management

This article explores the models of water resources management in irrigation systems. The article provides a classification of mathematical models for the optimal distribution of water resources. The work also substantiates the need to develop a new model of water resources management for distributed irrigation systems.

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