Optimal Temperature and Current Cycles for Curing of Composites Using Embedded Resistive Heating Elements

Curing is an important and time consuming step in the fabrication of thermosetting-matrix composite products. The use of embedded resistive heating elements providing supplemental heating from within the material being cured has been shown in previous studies (Ramakrishnan, Zhu, and Pitchumani, 2000, J. Manuf. Sci. Eng., 122, pp. 124–131; and Zhu and Pitchumani, 2000, Compos. Sci. Technol., 60, 2699–2712.) to offer significant improvements in cure cycle time and cure uniformity, due to the inside-out curing. This paper addresses the problem of determining the temperature and electrical current cycles, as well as the placement configuration of the conductive mats, for time-optimal curing of composites using embedded resistive heating elements. A continuous search simulated annealing optimization technique is utilized coupled with a numerical process simulation model to determine the optimal solutions for selected process constraints. Optimization results are presented over a range of material systems and different numbers of conductive mats to assess the effects of materials reactivity on the optimal number of conductive mats.

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A Hybrid Evolutionary Approach to Design Off-Grid Electrification Projects with Distributed Generation

Mathematical Problems in Engineering ◽

10.1155/2018/9135842 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

J. Avilés ◽

J. C. Mayo-Maldonado ◽

O. Micheloud

Keyword(s):

Distributed Generation ◽

Network Topology ◽

Optimization Technique ◽

Optimal Number ◽

Optimal Placement ◽

Evolutionary Approach ◽

Power Losses ◽

Network Configuration ◽

Optimal Network ◽

Particle Swarm Optimization Technique

A hybrid evolutionary approach is proposed to design off-grid electrification projects that require distributed generation (DG). The design of this type of systems can be considered as an NP-Hard combinatorial optimization problem; therefore, due to its complexity, the approach tackles the problem from two fronts: optimal network configuration and optimal placement of DG. The hybrid scheme is based on a particle swarm optimization technique (PSO) and a genetic algorithm (GA) improved with a heuristic mutation operator. The GA-PSO scheme permits finding the optimal network topology, the optimal number, and capacity of the generation units, as well as their best location. Furthermore, the algorithm must design the system under power quality requirements, network radiality, and geographical constraints. The approach uses GPS coordinates as input data and develops a network topology from scratch, driven by overall costs and power losses minimization. Finally, the proposed algorithm is described in detail and real applications are discussed, from which satisfactory results were obtained.

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Optimization of a Treatment System of Wastewater Streams for Electrochemical Cr(VI) Reduction: Selective versus Centralized Treatment

International Journal of Chemical Reactor Engineering ◽

10.1515/ijcre-2018-0076 ◽

2018 ◽

Vol 16 (11) ◽

Author(s):

Adrián López-Yañez ◽

Jorge Ramirez-Muñoz ◽

Alejandro Alonso ◽

Luis G. Cota ◽

Jhonny Pérez Montiel

Keyword(s):

Hexavalent Chromium ◽

Minimum Cost ◽

Electrical Energy ◽

Electrical Current ◽

Electrochemical Process ◽

Effluent Treatment ◽

Treated Wastewater ◽

Total Annual Cost ◽

Optimal Solutions ◽

Selective Treatment

Abstract The problem of the optimization of selective treatment systems of wastewater streams contaminated with hexavalent chromium [Cr(VI)] is investigated. In order to comply with the Mexican environmental norm of Cr(VI) for treated wastewater streams at minimum cost, a nonlinear programming (NLP) model for the electrochemical reduction of hexavalent chromium to trivalent chromium was developed. The model incorporates a variable reaction rate, which is a function of the Cr(VI) concentration and the electrical current density of the electrochemical process. For this purpose, a basic superstructure of the effluent treatment is proposed. The superstructure is composed of three continuous electrochemical reactors without recirculation, and it may produce either a series and/or parallel design topology. The NLP model was used to minimize the objective function, defined as the total annual cost (TAC), which includes the capital cost of each electrochemical reactor, the electrical energy cost and the cost of the treatment of the wastewater streams. In order to investigate the solution set of the proposed NLP model, i. e., to improve the possibilities of obtaining optimum solutions based on economic criteria, a multi-start algorithm was implemented. Two example problems are used to show the versatility of the model and different local optimal solutions were obtained for each case study. The results show that a selective treatment of wastewater streams based on the search of local optimal solutions yields significant savings with respect to a centralized treatment design.

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Optimization of Smart Wells in the St. Joseph Field

SPE Reservoir Evaluation & Engineering ◽

10.2118/123563-pa ◽

2010 ◽

Vol 13 (04) ◽

pp. 588-595 ◽

Cited By ~ 10

Author(s):

G. M. van Essen ◽

J. D. Jansen ◽

D. R. Brouwer ◽

S. G. Douma ◽

M. J. Zandvliet ◽

...

Keyword(s):

Optimization Technique ◽

Gas Injection ◽

Optimal Number ◽

Control Parameters ◽

Operational Strategies ◽

Sector Model ◽

Optimization Study ◽

Smart Wells ◽

Gradient Based ◽

Reservoir Simulator

Summary The St. Joseph field has been on production since September 1981 under natural depletion supported by crestal gas injection. As part of a major redevelopment study, the scope for waterflooding was addressed using "smart" completions with multiple inflow control valves (ICVs) in the wells to be drilled for the redevelopment. Optimal control theory was used to optimize monetary value over the remaining producing life of the field, and in particular to select the optimal number of ICVs, the optimal configuration of the perforation zones, and the optimal operational strategies for the ICVs. A gradient-based optimization technique was implemented in a reservoir simulator equipped with the adjoint functionality to compute gradients of an objective function with respect to control parameters. For computational reasons, an initial optimization study was performed on a sector model, which showed promising results.

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A Novel Modeling Approach for Solving the Cell Formation Problem

Volume 2: Advanced Manufacturing ◽

10.1115/imece2018-88128 ◽

2018 ◽

Author(s):

Taras Dmytryshyn ◽

Mohamed Ismail ◽

Ola Rashwan

Keyword(s):

Optimization Technique ◽

Cell Formation ◽

Solution Process ◽

Cell Formation Problem ◽

Optimal Solutions ◽

Modeling Approach ◽

Formation Problem ◽

Future Extension ◽

User Friendly ◽

Problem Data

This paper presents a new modeling approach called Progressive Modeling (PM) and demonstrates it by solving the Cell Formation Problem (CFP). In this paper, the Progressive Modeling (PM), a component-based optimization technique, is used to solve the cell formation problem (CFP). This novel solution algorithm is utilized to find optimal or near-optimal solutions. A user-friendly Windows application is presented to capture the problem data, demonstrate the solution process, and display the results. A benchmark problem in the literature is solved and presented in this paper. The paper concludes by demonstrating the efficiency of the new modeling approach and its future extension.

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Kinematics Optimization of a 3-SPS Parallel Redundant Motion Mechanism Using Conformal Geometric Algebra

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.789-790.889 ◽

2015 ◽

Vol 789-790 ◽

pp. 889-895

Author(s):

Jahng Hyon Park ◽

Jeseok Kim ◽

Jin Han Jeong

Keyword(s):

High Speed ◽

Degrees Of Freedom ◽

Optimization Technique ◽

Optimal Solution ◽

Inverse Kinematic ◽

Fast Reaction ◽

Inverse Kinematic Problem ◽

Time Optimal ◽

Parallel Link ◽

Spherical Parallel Manipulator

In this paper, an actuation mechanism for high-speed aiming of a target is proposed. The mechanism is a 3DOF-SPS (spherical-prismatic-spherical) parallel manipulator and can be used for a missile defense system with a fast reaction time. This type of parallel mechanism has high rigidity against external disturbances and accordingly high stiffness and precision. The target aiming requires 2 degrees of freedom and this 3 DOF mechanism has one redundancy. For fast manipulation of the proposed mechanism, the redundancy can be exploited and an optimal solution can be found out of the infinite number of inverse kinematic solutions. For finding a near time-optimal solution, a cost function is formulated considering displacement of each parallel link and an optimization technique is used for solution of the inverse kinematic problem.

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Numerical Analysis of Constrained, Time-Optimal Satellite Reorientation

Mathematical Problems in Engineering ◽

10.1155/2012/769376 ◽

2012 ◽

Vol 2012 ◽

pp. 1-19 ◽

Cited By ~ 2

Author(s):

Robert G. Melton

Keyword(s):

Numerical Analysis ◽

High Intensity ◽

Numerical Solutions ◽

Pseudospectral Method ◽

Optimal Solutions ◽

Path Constraints ◽

Time Optimal ◽

Stage Process ◽

Legendre Pseudospectral Method ◽

Method Show

Previous work on time-optimal satellite slewing maneuvers, with one satellite axis (sensor axis) required to obey multiple path constraints (exclusion from keep-out cones centered on high-intensity astronomical sources) reveals complex motions with no part of the trajectory touching the constraint boundaries (boundary points) or lying along a finite arc of the constraint boundary (boundary arcs). This paper examines four cases in which the sensor axis is either forced to follow a boundary arc, or has initial and final directions that lie on the constraint boundary. Numerical solutions, generated via a Legendre pseudospectral method, show that the forced boundary arcs are suboptimal. Precession created by the control torques, moving the sensor axis away from the constraint boundary, results in faster slewing maneuvers. A two-stage process is proposed for generating optimal solutions in less time, an important consideration for eventual onboard implementation.

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A NEW PROBABILISTIC ALGORITHM FOR SOLVING A CLASS OF SINGLE OR MULTI-OBJECTIVE OPTIMAL PROBLEMS

Science and Technology Development Journal ◽

10.32508/stdj.v12i11.2308 ◽

2009 ◽

Vol 12 (11) ◽

pp. 11-26

Author(s):

Hao Van Tran ◽

Thong Huu Nguyen

Keyword(s):

Numerical Optimization ◽

Optimization Problems ◽

Optimization Technique ◽

Fixed Number ◽

Optimal Solutions ◽

Multi Objective Optimization ◽

Probabilistic Algorithm ◽

Multi Objective ◽

Single Objective

We consider a class of single-objective optimization problems which haves the character: there is a fixed number k (1≤k<n) that is independent of the size n of the problem such that if we only need to change values of k variables then it has the ability to find a better solution than the current one, let us call it Ok. In this paper, we propose a new numerical optimization technique, Search Via Probability (SVP) algorithm, for solving single objective optimization problems of the class Ok. The SVP algorithm uses probabilities to control the process of searching for optimal solutions. We calculate probabilities of the appearance of a better solution than the current one on each of iterations, and on the performance of SVP algorithm we create good conditions for its appearance. We tested this approach by implementing the SVP algorithm on some test single-objective and multi objective optimization problems, and we found good and very stable results.

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