scholarly journals An object-oriented development environment to optimally design cyclic storage systems

2015 ◽  
Vol 17 (4) ◽  
pp. 534-550
Author(s):  
Mohammadamin Jahanpour ◽  
Abbas Afshar ◽  
Samuel Sandoval Solis
Keyword(s):  
Large Scale ◽  
Pareto Front ◽  
Storage System ◽  
Object Oriented ◽  
Design Parameters ◽  
Objective Functions ◽  
Development Environment ◽  
Nsga Ii ◽  
Cyclic Storage ◽  
Development Alternatives

Cyclic storage system (CSS) is defined as physically interconnected and operationally integrated surface water and groundwater subsystems with full direct interactions between the subsystems. Mathematical development and implementation of a CSS model is very complex and all previous works are fully case dependent with a minimum possibility of generalization. This article proposes an integrated development environment called CSSDev, which assists researchers to create and design object-oriented CSS models more easily. Using CSSDev, researchers may skip regeneration of repetitive simulation codes for common elements of a CSS. CSSDev employs NSGA-II to optimally select the design parameters of the models. Two objective functions of the optimization problem are system's total costs and total loss associated with the development alternatives. A real-world large-scale CSS has been modeled and optimized to illustrate the performance of CSSDev. The final Pareto-front is presented and two selected solutions from the set of optimal non-dominated ones are evaluated and discussed.

scholarly journals Optimal Array Reconfiguration of a PV Power Plant for Frequency Regulation of Power Systems

2021 ◽  
Vol 9 ◽  
Author(s):  
Tingyi He ◽  
Shengnan Li ◽  
Yiping Chen ◽  
Shuijun Wu ◽  
Chuangzhi Li
Keyword(s):  
Power Plant ◽  
Power Systems ◽  
Pareto Front ◽  
Frequency Control ◽  
Storage System ◽  
Energy Storage System ◽  
Automatic Generation ◽  
Frequency Regulation ◽  
Nsga Ii ◽  
Partial Shading

This paper establishes a novel optimal array reconfiguration (OAR) of a PV power plant for secondary frequency control of automatic generation control (AGC). Compared with the existing studies, the proposed OAR can further take the AGC signal responding into account except the maximum power output, in which the battery energy storage system is used to balance the power deviation between the AGC signals and the PV power outputs. Based on these two conflicted objects, the OAR is formulated as a bi-objective optimization. To address this problem, the efficient non-dominated sorting genetic algorithm II (NSGA-II) is designed to rapidly obtain an optimal Pareto front due to its high optimization efficiency. The decision-making method called VIKOR is employed to determine the best compromise solution from the obtained Pareto front. To verify the effectiveness of the proposed bi-objective optimization of OAR, three case studies with fixed, step-increasing, and step-decreasing AGC signals are carried out on a 10 × 10 total-cross-tied PV arrays under partial shading conditions.

Multi-Objective Geometric Optimization of Elliptical-Toothed Gerotor Pumps for Kinematics and Wear by Genetic Algorithm

2018 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Pareto Front ◽  
Adhesive Wear ◽  
Geometric Optimization ◽  
Objective Functions ◽  
Computationally Efficient ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Flow Ripple ◽  
Gerotor Pumps

A computationally efficient gerotor gear generation algorithm has been developed that creates elliptical-toothed gerotor gear profiles, identifies conditions to guarantee a feasible geometry, evaluates several performance objectives, and is suitable to use for geometric optimization. Five objective functions are used in the optimization: minimize pump size, flow ripple, adhesive wear, subsurface fatigue (pitting), and tooth tip leakage. The gear generation algorithm is paired with the NSGA-II optimization algorithm to minimize each of the objective functions subject to the constraints to define a feasible geometry. The genetic algorithm is run with a population size of 1000 for a total of 500 generations, after which a clear Pareto front is established and displayed. A design has been selected from the Pareto front which is a good compromise between each of the design objectives and can be scaled to any desired displacement. The results of the optimization are also compared to two profile geometries found in literature. Two alternative geometries are proposed that offer much lower adhesive wear while respecting the size constraints of the published profiles and are thought to be an improvement in design.

Dynamic Performance Analysis of Large-Scale Packed Bed Truncated Conical Thermal Energy Storage

2019 ◽  
Author(s):  
Shobhana Singh ◽  
Kim Sørensen
Keyword(s):  
Energy Storage ◽  
Large Scale ◽  
Storage System ◽  
Transient Behavior ◽  
Packed Bed ◽  
Energy Storage System ◽  
Heat Transfer Fluid ◽  
Design Parameters ◽  
Wide Range ◽  
Dynamic Performance Analysis

Abstract In the present paper, a high-temperature packed bed energy storage system of volume 175,000m3 is numerically investigated. The system is a underground packed bed of truncated conical shape, which comprises of rocks as a storage medium and air as a heat transfer fluid. A one-dimensional, two-phase model is developed to simulate the transient behavior of the storage. The developed model is used to conduct a parametric study with a wide range of design parameters to investigate the change in performance during both charging and discharging operation. Results show that the model satisfactorily predicts the dynamic behavior, and the truncated conical shaped storage with a rock diameter of 3cm, insulation thickness up to 0.6m and charging-discharging rate of 553kg/s leads to lower thermal losses and higher energy efficiencies. The paper provides useful insight into the transient performance and efficiency of a large-scale packed bed energy storage system within the range of parameters investigated.

Effect of Design Parameters on the Performance of Thermal Energy Storage Systems

Solar Energy ◽  
2004 ◽  
Author(s):  
Gregor P. Henze
Keyword(s):  
Energy Storage ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Large Scale ◽  
Control Strategies ◽  
Storage System ◽  
Operating Cost ◽  
Energy Storage System ◽  
Design Parameters ◽  
Wide Range

This paper describes simulation-based results of a large-scale investigation of a commercial cooling plant including a thermal energy storage system. A cooling plant with an ice-on-coil system with external melt and a reciprocating compressor operating in a large office building was analyzed under four different control strategies. Optimal control as the strategy that minimizes the total operating cost (demand and energy charges) served as a benchmark to assess the performance of the three conventional controls. However, all control strategies depend on properly selected design parameters. The storage and chiller capacities as the primary design parameters were varied over a wide range and the dependence of the system’s cost saving performance on these parameters was evaluated.

Mathematical Modeling and Multi-Objective Optimization of a Mini-Channel Heat Exchanger Via Genetic Algorithm

2013 ◽  
Vol 5 (3) ◽  
Author(s):  
Tommaso Selleri ◽  
Behzad Najafi ◽  
Fabio Rinaldi ◽  
Guido Colombo
Keyword(s):  
Genetic Algorithm ◽  
Heat Exchanger ◽  
Multiobjective Optimization ◽  
Pareto Front ◽  
Optimization Procedure ◽  
Design Parameters ◽  
Geometrical Parameters ◽  
Objective Functions ◽  
Pressure Drops ◽  
Overall Heat Transfer Coefficient

In the present paper a mathematical model for a mini-channel heat exchanger is proposed. Multiobjective optimization using genetic algorithm is performed in the next step in order to obtain a set of geometrical design parameters, leading to minimum pressure drops and maximum overall heat transfer coefficient. Multiobjective optimization procedure provides a set of optimal solutions, called Pareto front, each of which is a trade-off between the objective functions and can be freely selected by the user according to the specifications of the project. A sensitivity analysis is also carried out to study the effects of different geometrical parameters on the considered functions. The whole system has been modeled based on advanced experimental correlations in matlab environment using a modular approach.

scholarly journals Multi-Objective Design Optimization of the Leg Mechanism for a Piping Inspection Robot

2014 ◽  
Author(s):  
Renaud Henry ◽  
Damien Chablat ◽  
Mathieu Porez ◽  
Frédéric Boyer ◽  
Daniel Kanaan
Keyword(s):  
Nuclear Power ◽  
Pareto Front ◽  
Design Parameters ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Dimensional Synthesis ◽  
Force Factor ◽  
Multi Objective ◽  
Inspection Robot ◽  
Contact Points

This paper addresses the dimensional synthesis of an adaptive mechanism of contact points ie a leg mechanism of a piping inspection robot operating in an irradiated area as a nuclear power plant. This studied mechanism is the leading part of the robot sub-system responsible of the locomotion. Firstly, three architectures are chosen from the literature and their properties are described. Then, a method using a multi-objective optimization is proposed to determine the best architecture and the optimal geometric parameters of a leg taking into account environmental and design constraints. In this context, the objective functions are the minimization of the mechanism size and the maximization of the transmission force factor. Representations of the Pareto front versus the objective functions and the design parameters are given. Finally, the CAD model of several solutions located on the Pareto front are presented and discussed.

MULTIOBJECTIVE OPTIMIZATION OF PROCESS PLANT USING GENETIC ALGORITHM

2006 ◽  
Vol 06 (03) ◽  
pp. 315-327 ◽  
Author(s):  
L. GOVINDARAJAN ◽  
T. KARUNANITHI
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Multiobjective Optimization ◽  
Large Scale ◽  
Raw Material ◽  
Fine Tuning ◽  
Design Parameters ◽  
Nsga Ii ◽  
Process Plant ◽  
Mathematical Techniques

The optimal design of large-scale process plant is difficult due to the presence of Pareto sets or nondominated solutions. Many conventional and advanced mathematical techniques had been adopted which have their own limitations in solving the complex design problem. In this paper, nondominant-sorted genetic algorithms NSGA and NSGA-II have been adopted for the optimal design of complex Williams–Otto model process plant. The plant consists of a reactor, separation system consisting of heat exchanger, decanter and distillation column. Multiobjective optimization is used to maximize the profit, i.e. the return on investment, to maintain lesser use of costlier raw material and lesser disposal of the waste byproducts. So NSGA-II is employed in this study as an effective replacement for NSGA, classical genetic algorithm, conventional and traditional methods of optimization in solving multiobjective process design problems and to achieve fine-tuning of variables in determining Pareto optimal design parameters. NSGA-II method finding global optimal front has a significant effect on the design of control system for the real time and continuous robust control of complex process plant as each target vector provides proper direction and drives the process to multiobjective optimum conditions.

Optimal Design for Heavy Forging Robot Grippers

2010 ◽  
Vol 44-47 ◽  
pp. 743-747 ◽  
Author(s):  
Qun Ming Li ◽  
Qing Hua Qin ◽  
Shi Wei Zhang ◽  
Hua Deng
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Large Scale ◽  
Similarity Theory ◽  
Optimal Solution ◽  
Small Scale ◽  
Objective Functions ◽  
Force Transmission ◽  
Nsga Ii ◽  
Pareto Fronts

This paper analyzes three typical mechanisms of heavy forging robot grippers: pulling with a sliding block including short- and long-leveraged grippers and pushing leveraged grippers, and uses multi-objective evolutionary genetic algorithm to design the optimal forging robot grippers. The decision variables are defined according to the geometrical dimensions of the heavy grippers, and four objective functions are defined according to gripping forces and force transmission relationships between the joints, and the constraints are yielded by the physical conditions and the structure of the grippers. Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to solve the optimization problem. Normalized weighting objective functions are used to select the best optimal solution from Pareto optimal fronts. The Pareto fronts and optimal results are compared and analyzed. An optimal model of forging robot gripper is designed. The results show the effectiveness of the optimal design. Based on similarity theory, optimum dimensions from small scale forging grippers to large scale ones can be designed, and from model to prototype experiment to test the physical features is possible.

Different nanofluids as coolant in heat exchanger network: Thermoeconomic modeling and multi-objective optimization

2021 ◽  
pp. 095440892110080
Author(s):  
Hassan Hajabdollahi ◽  
Babak Masoumpour
Keyword(s):  
Heat Exchanger ◽  
Mass Flow ◽  
Pareto Front ◽  
Design Parameters ◽  
Total Annual Cost ◽  
Objective Functions ◽  
Tube Heat Exchanger ◽  
Method Optimization ◽  
Mass Flow Rates ◽  
Cold Mass

Modeling and optimization of a multi tube heat exchanger (MTHE) network considering the effects of different nanoparticles on the tube side are carried out using Fast and elitist non-dominated sorting genetic algorithm. After thermal modeling in [Formula: see text] method, optimization is performed by increasing the effectiveness and decreasing total annual cost as two objective functions using eight design parameters such as number of MTHE and particles volumetric concentration. In addition, optimization is performed at three various cold mass flow rates and different nanoparticles including Al2O3, CuO and ZrO2 and results are compared with the base fluid (water). For the reliability of the present code, the modeling results are validated with the results obtained from both the numerical and experimental model. The results show that the optimal Pareto front is improved in nanoparticles case, and the rate of improvement in CuO nanoparticles case, especially in higher effectiveness and lower cold mass flow rate is more significant compared with the other studied cases. In addition, because of improvement in the thermal performance of MTHE network with nanoparticles, the heat transfer surface area and consequently the total volume of MTHE network for the fixed values of effectiveness are noticeably reduced. Finally, the effects of design parameters versus effectiveness are demonstrated and discussed.

scholarly journals Finite Element Based Overall Optimization of Switched Reluctance Motor Using Multi-Objective Genetic Algorithm (NSGA-II)

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 576
Author(s):  
Mohamed El-Nemr ◽  
Mohamed Afifi ◽  
Hegazy Rezk ◽  
Mohamed Ibrahim
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Optimal Design ◽  
Switched Reluctance Motor ◽  
Design Parameters ◽  
Objective Functions ◽  
Nsga Ii ◽  
Switched Reluctance ◽  
Multi Objective ◽  
Reluctance Motor

The design of switched reluctance motor (SRM) is considered a complex problem to be solved using conventional design techniques. This is due to the large number of design parameters that should be considered during the design process. Therefore, optimization techniques are necessary to obtain an optimal design of SRM. This paper presents an optimal design methodology for SRM using the non-dominated sorting genetic algorithm (NSGA-II) optimization technique. Several dimensions of SRM are considered in the proposed design procedure including stator diameter, bore diameter, axial length, pole arcs and pole lengths, back iron length, shaft diameter as well as the air gap length. The multi-objective design scheme includes three objective functions to be achieved, that is, maximum average torque, maximum efficiency and minimum iron weight of the machine. Meanwhile, finite element analysis (FEA) is used during the optimization process to calculate the values of the objective functions. In this paper, two designs for SRMs with 8/6 and 6/4 configurations are presented. Simulation results show that the obtained SRM design parameters allow better average torque and efficiency with lower iron weight. Eventually, the integration of NSGA-II and FEA provides an effective approach to obtain the optimal design of SRM.

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