Building representative geological models of reservoirs is a complex task, especially while using traditional geostatistical modeling methods due to data limitations. Stratigraphic Forward Modeling (SFM) enhances the ...
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Building representative geological models of reservoirs is a complex task, especially while using traditional geostatistical modeling methods due to data limitations. Stratigraphic Forward Modeling (SFM) enhances the accuracy of models by incorporating geologic and depositional concepts, resulting in greater applicability. However, the method struggles with well data integration and definition of simulation input parameters which are not easily drawn from usual available data or conceptual modeling. Hence, there are uncertainties related to SFM input parameters and the reliability of results. In this work, SFM multi-realizations performed by DionisosFlowTM were analyzed through an objective function that measures similarity between facies successions (stratigraphic correlation objective function - SCOOF) to compose an empirical methodology that performs the adjustment of SFM models to well data. A set of scenarios was assembled by varying a group of selected uncertain parameters. These scenarios were submitted to SCOOF calculation and parameter values were taken from those that gave lower SCOOF values. By re-parameterizing the initial model with chosen values, thickness and lithology deposition improvements in wells were obtained and validated by the decline of objective function values from the initial to the final model.
In the context of model updating of bridge structures, dynamic approaches are currently dominant. This is mainly due to the opportunity of performing dynamic tests under environmental and traffic loadings, without put...
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In the context of model updating of bridge structures, dynamic approaches are currently dominant. This is mainly due to the opportunity of performing dynamic tests under environmental and traffic loadings, without putting the bridges out of service. Several techniques have been proposed in the literature to control and address the relevant model updating workflow. These methods typically consider the structural frequencies, or a combination of frequencies with vibration modes. Dissipative properties are, on the contrary, more rarely considered in updating procedures, given their strong dependence on the amplitude of the vibrations and on the type of forcing load. In this work, six ruling objective functions are considered for the dynamic model updating of girder bridge structures. The first one, taken from the literature, is a widely used function based on discrepancies among numerical and experimental frequencies. Two additional functions, also derived from the existing literature, are subsequently considered: one focuses on vibration modes, utilizing the Modal Assurance Criterion (MAC), and the other incorporates both structural frequencies and mode shapes, deploying the Modal Flexibility Matrix (MFM). Three novel objective functions are introduced, which are adaptations of the previously mentioned ones, with alternative applications of MAC and MFM. These six functions are analyzed and discussed through two comprehensive experimental case studies, in which the relative weights of the specific function terms are also investigated. A quantitative selection criterion is proposed and examined in order to choose the most suitable objective function based on identifiability. The method implementation, leveraging second-order derivatives, is executed via a finite difference scheme.
In this work, a general method using exergy analysis has been proposed to achieve a compromise between heat transfer effectiveness and pressure loss in heat transfer optimization problems involving internal channels. ...
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In this work, a general method using exergy analysis has been proposed to achieve a compromise between heat transfer effectiveness and pressure loss in heat transfer optimization problems involving internal channels. The proposed method is applied to the design optimization of a channel roughened by staggered arrays of dimples for heat transfer augmentation. Optimization is performed using surrogate-based optimization techniques and three-dimensional Reynolds-averaged Navier-Stokes analysis. Three nondimensional design variables are defined using the dimpled channel height, dimple print diameter, dimple spacing, and dimple depth. The objective function is defined as the net exergy gain considering the exergy gain by heat transfer, and exergy losses generated by friction and heat transfer. Twenty design points are generated using Latin hypercube sampling, and the Kriging model is used as a surrogate model to approximate the objective function values in the design space. Through optimization, the objective function is successfully improved with respect to the reference geometry. (C) 2012 Elsevier Ltd. All rights reserved.
Hard C-means (HCM) is one of the most widely used partitive clustering methods and was extended to rough C-means (RCM) by referencing to the perspective of rough set theory to deal with the certain, possible, and unce...
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Hard C-means (HCM) is one of the most widely used partitive clustering methods and was extended to rough C-means (RCM) by referencing to the perspective of rough set theory to deal with the certain, possible, and uncertain belonging of object to clusters. Furthermore, rough set C-means (RSCM) and rough membership C-means (RMCM) have been proposed as clustering models on an approximation space considering the granularity of the universe (object space) based on binary relations. Although these rough set-based C-means methods are practical, they are not formulated based on objective functions, but are built on heuristic schemes. objective function-based methods can be a basis for discussion of the validity of clustering and further theoretical developments. In this paper, we propose a novel RMCM framework, which is called RMCM version 2 (RMCM2), based on an objective function. The objective function is designed to derive the same updating rule for cluster centers as in RMCM. We demonstrate the characteristics of RMCM2 by visualizing cluster boundaries on a grid point dataset. Furthermore, we verify the clustering performance of RMCM2 through numerical experiments by using real-world datasets. (c) 2020 The Author(s). Published by Elsevier Inc.
Ambiguities in geophysical inversion results are always present. How these ambiguities appear in most cases open to interpretation. It is interesting to investigate ambiguities with regard to the parameters of the mod...
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Ambiguities in geophysical inversion results are always present. How these ambiguities appear in most cases open to interpretation. It is interesting to investigate ambiguities with regard to the parameters of the models under study. Residual function Dispersion Map (RFDM) can be used to differentiate between global ambiguities and local minima in the objective function. We apply RFDM to Vertical Electrical Sounding (VES) and TEM Sounding inversion results. Through topographic analysis of the objective function we evaluate the advantages and limitations of electrical sounding data compared with TEM sounding data, and the benefits of joint inversion in comparison with the individual methods. The RFDM analysis proved to be a very interesting tool for understanding the joint inversion method of VES/TEM. Also the advantage of the applicability of the RFDM analyses in real data is explored in this paper to demonstrate not only how the objective function of real data behaves but the applicability of the RFDM approach in real cases. With the analysis of the results, it is possible to understand how the joint inversion can reduce the ambiguity of the methods. (C) 2017 Published by Elsevier B.V.
The use of the optimum series of heat exchangers rather than ones individually produced may benefit both manufacturers and users. A method for the optimization of a series of heat exchangers has been presented. A poss...
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The use of the optimum series of heat exchangers rather than ones individually produced may benefit both manufacturers and users. A method for the optimization of a series of heat exchangers has been presented. A possible decrease of around 20% in the capital cost of apparatus produced in a series has been estimated in comparison with individually produced units. The method can be extended to other series of apparatus used by the chemical process industries.
Partitioning and allocation of relations is an important component of the distributed database design. Several approaches (and algorithms) have been proposed for clustering data for pattern classification and for part...
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Partitioning and allocation of relations is an important component of the distributed database design. Several approaches (and algorithms) have been proposed for clustering data for pattern classification and for partitioning relations in distributed databases. Most of the approaches used for classification use square-error criterion. In contrast, most of the approaches proposed for partitioning of relations are either ad hoc solutions or solutions for special cases (e.g., binary vertical partitioning). In this paper, we first highlight the differences between the approaches taken for pattern classification and for distributed databases. Then an objective function for vertical partitioning of relations is derived using the square-error criterion commonly used in data clustering. The objective function derived generalizes and subsumes earlier work on vertical partitioning. Furthermore, the approach proposed in this paper is shown to be useful for comparing previously developed algorithms for vertical partitioning. The objective function has also been extended to include additional information, such as transaction types, different local and remote accessing costs and replication. Finally, we discuss the implementation of a distributed database design testbed.
Clustering is typically applied for data exploration when there are no or very few labeled data available. The goal is to find groups or clusters of like data. The clusters will be of interest to the person applying t...
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Clustering is typically applied for data exploration when there are no or very few labeled data available. The goal is to find groups or clusters of like data. The clusters will be of interest to the person applying the algorithm. An objective function-based clustering algorithm tries to minimize (or maximize) a function such that the clusters that are obtained when the minimum/maximum is reached are homogeneous. One needs to choose a good set of features and the appropriate number of clusters to generate a good partition of the data into maximally homogeneous groups. objective functions for clustering are introduced. Clustering algorithms generated from the given objective functions are shown, with a number of examples of widely used approaches discussed. (c) 2012 Wiley Periodicals, Inc.
This paper presents the adaptation and implementation of the objective function in routing protocol RPL (Routing Protocol for Low Power and Lossy Networks) restating in terms of the status of the connection for better...
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This paper presents the adaptation and implementation of the objective function in routing protocol RPL (Routing Protocol for Low Power and Lossy Networks) restating in terms of the status of the connection for better performance quality of service offered. In this way, a new routing metric that improves the indicators quality of service was established: network lifetime, packet delivery and energy savings of LLN networks outlined in the framework of the Internet of Things (IoT);all within the functional specifications for these networks of Internet and Engineering Working Group (In English: Internet Engineering Task Force - IETF). It was performed a series of tests and different simulations in Cooja, where BF-ETX generates better results in terms of network latency and power consumption in densely deployed networks and large-scale front MRHOF. This way, it can achieve a better performance of LLNS to large scale in terms of quality of service compared to routing problems.
The Model Based Predictive Control (MPC) is an advanced control technique that can deal with of multivariable systems with interactions and considerable dead times, nonlinearities and restrictions on the variables. Di...
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The Model Based Predictive Control (MPC) is an advanced control technique that can deal with of multivariable systems with interactions and considerable dead times, nonlinearities and restrictions on the variables. Different types of objective functions can be used in the MPC control algorithm with specific parameter settings for each type of objective function. In this work a MPC control strategy for distillation column using an objective function with economic cost was implemented. The MPC controller with the objective function proposed worked properly, stabilizing the distillation column when it has undergone a change in the setpoint and also a series of disturbances in the feed. Using energy point of view, the implemented controller is more efficient than the standard MPC controller.
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