Two-Dimensional Mathematical Optimization Approach for Well Placement and Fracture Design of Shale Reservoirs

2018 ◽  
pp. 185-214
Author(s):  
Ahmed Alzahabi ◽  
Mohamed Y. Soliman
Keyword(s):  
Mathematical Optimization ◽  
Optimization Approach ◽  
Two Dimensional ◽  
Well Placement ◽  
Shale Reservoirs

scholarly journals Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach

Omega ◽  
2020 ◽  
Vol 96 ◽  
pp. 102068 ◽  
Author(s):  
Sandra Benítez-Peña ◽  
Peter Bogetoft ◽  
Dolores Romero Morales
Keyword(s):  
Feature Selection ◽  
Data Envelopment Analysis ◽  
Mathematical Optimization ◽  
Optimization Approach ◽  
Data Envelopment

scholarly journals Read-across predictions of nanoparticle hazard endpoints: a mathematical optimization approach

2019 ◽  
Vol 1 (9) ◽  
pp. 3485-3498 ◽  
Author(s):  
Dimitra-Danai Varsou ◽  
Antreas Afantitis ◽  
Georgia Melagraki ◽  
Haralambos Sarimveis
Keyword(s):  
Genetic Algorithms ◽  
Mathematical Optimization ◽  
Optimization Approach

Development of a novel read-across methodology for the prediction of toxicity related endpoints of nanoparticles based on genetic algorithms.

scholarly journals Hybrid GA-SOCP Approach for Placement and Sizing of Distributed Generators in DC Networks

2020 ◽  
Vol 10 (23) ◽  
pp. 8616 ◽  
Author(s):  
Oscar Danilo Montoya ◽  
Walter Gil-González ◽  
Luis Fernando Grisales-Noreña
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Mathematical Optimization ◽  
Optimal Solution ◽  
Distribution Networks ◽  
Optimization Approach ◽  
Global Optimal Solution ◽  
Distributed Generators ◽  
Second Order Cone ◽  
Global Optimal

This research addresses the problem of the optimal location and sizing distributed generators (DGs) in direct current (DC) distribution networks from the combinatorial optimization. It is proposed a master–slave optimization approach in order to solve the problems of placement and location of DGs, respectively. The master stage applies to the classical Chu & Beasley genetic algorithm (GA), while the slave stage resolves a second-order cone programming reformulation of the optimal power flow problem for DC grids. This master–slave approach generates a hybrid optimization approach, named GA-SOCP. The main advantage of optimal dimensioning of DGs via SOCP is that this method makes part of the exact mathematical optimization that guarantees the possibility of finding the global optimal solution due to the solution space’s convex structure, which is a clear improvement regarding classical metaheuristic optimization methodologies. Numerical comparisons with hybrid and exact optimization approaches reported in the literature demonstrate the proposed hybrid GA-SOCP approach’s effectiveness and robustness to achieve the global optimal solution. Two test feeders compose of 21 and 69 nodes that can locate three distributed generators are considered. All of the computational validations have been carried out in the MATLAB software and the CVX tool for convex optimization.

Applying Ensemble Kalman Filter to Transonic Flows Through a Two-Dimensional Turbine Cascade

2021 ◽  
Vol 143 (12) ◽  
Author(s):  
Sasuga Ito ◽  
Masato Furukawa ◽  
Kazutoyo Yamada ◽  
Kaito Manabe
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Numerical Solutions ◽  
Stokes Equations ◽  
Three Dimensional ◽  
Navier Stokes ◽  
Optimization Approach ◽  
Two Dimensional ◽  
Turbine Cascade ◽  
Eddy Simulation

Abstract Turbulence is one of the most important phenomena in fluid dynamics. Large eddy simulation (LES) generally allows us to analyze smaller eddies than when using simulations based on unsteady Reynolds-averaged Navier–Stokes equations (URANS). In addition, the numerical solutions of LES show good agreements with experiments and numerical solutions based on direct numerical simulation. URANS simulations are, however, frequently used in academia and industry because LES computations are much more expensive compared with URANS simulations. In this investigation, an optimization of unsolved coefficients of the k–ω two equations model is performed on the transonic flow around T106A low-pressure turbine cascade to improve the accuracy of turbulence prediction with URANS. For the optimization approach, two-dimensional URANS is combined with ensemble Kalman filter which is one of the data assimilation techniques. In the assimilation process, a time- and spanwise-averaged LES result is used as pseudo-experimental data. Three-dimensional URANS simulations are performed for the evaluation of the optimization effect. URANS simulations are also applied to a different turbine cascade flow for the evaluation of the robustness of the optimized coefficients. These URANS results confirmed that the optimized coefficients improve the accuracy of turbulence prediction.

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