Multi-Objective Optimization of Double-Tube Once-Through Steam Generator

2012 ◽  
Vol 134 (7) ◽  
Author(s):  
Xinyu Wei ◽  
Chunhui Dai ◽  
Yun Tai ◽  
Fuyu Zhao
Keyword(s):  
Optimal Design ◽  
Pressure Drop ◽  
Steam Generator ◽  
Flow Distribution ◽  
Tube Length ◽  
Design Parameters ◽  
Straight Tube ◽  
Nsga Ii ◽  
Primary Fluid ◽  
Helical Tube

This paper presents a double-tube once-through steam generator (DOTSG) consisting of the outer straight tube and the inner helical tube. The tube length and pressure drop of are important parameters in optimal design of DOTSG. For optimal design of such a system, it was modeled to estimate its tube length and pressure drop. Pitch of inner helical tube, flow distribution ratio of the primary fluid, and tube assemblage are considered as design parameters. Then fast and elitist nondominated sorting genetic algorithm-II (NSGA-II) method was applied to find the optimum values of design parameters. In the presented optimal design approach, the tube length and the total pressure drop are two objective functions. The results of optimal designs were a set of multiple optimum solutions, called “Pareto optimal solutions.” The sensitivity analysis of change in optimum tube length and pressure drop with change in design parameters of the DOTSG is also performed and the results are reported.

Multi-Objective Optimal Design of Passive Suspension System With Inerter Damper

2018 ◽  
Author(s):  
Xiaotian Xu ◽  
Yousef Sardahi ◽  
Chenyu Zheng
Keyword(s):  
Optimal Design ◽  
Suspension System ◽  
Design Parameters ◽  
Head Acceleration ◽  
Crest Factor ◽  
Nsga Ii ◽  
Passive Suspension ◽  
Trade Offs ◽  
Passive Suspension System ◽  
Unsprung Mass

This paper presents a many-objective optimal design of a four-degree-of-freedom passive suspension system with an inerter device. In the optimization process, four objectives are considered: passenger’s head acceleration (HA), crest factor (CF), suspension deflection (SD), and tire deflection (TD). The former two objectives are important for the health and comfort of the driver and the latter two quantify the suspension system performance. The spring ks and damping cs constants between the sprung mass and unsprung mass, the inertance coefficient B, and the tire spring constant ky are considered as design parameters. The non-dominated sorting genetic algorithm (NSGA-II) is used to solve this optimization problem. The results show that there are many optimal trade-offs among the design objectives that could be applicable to suspension design in the industry.

scholarly journals Sensitivity Evaluation of AP1000 Nuclear Power Plant Best Estimation Model

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Hao Shi ◽  
Qi Cai ◽  
Yuqing Chen
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Pressure Drop ◽  
Temperature Variation ◽  
Steam Generator ◽  
Nuclear Power ◽  
Channel Number ◽  
Design Parameters ◽  
Coolant Temperature ◽  
Node Number

The best estimation process of AP1000 Nuclear Power Plant (NPP) requires proper selections of parameters and models so as to obtain the most accurate results compared with the actual design parameters. Therefore, it is necessary to identify and evaluate the influences of these parameters and modeling approaches quantitatively and qualitatively. Based on the best estimate thermal-hydraulic system code RELAP5/MOD3.2, sensitivity analysis has been performed on core partition methods, parameters, and model selections in AP1000 Nuclear Power Plant, like the core channel number, pressurizer node number, feedwater temperature, and so forth. The results show that core channel number, core channel node number, and the pressurizer node number have apparent influences on the coolant temperature variation and pressure drop through the reactor. The feedwater temperature is a sensitive factor to the Steam Generator (SG) outlet temperature and the Steam Generator outlet pressure. In addition, the cross-flow model nearly has no effects on the coolant temperature variation and pressure drop in the reactor, in both the steady state and the loss of power transient. Furthermore, some fittest parameters with which the most accurate results could be obtained have been put forward for the nuclear system simulation.

Optimal Design Parameters of Cab’s Isolation System for Vibratory Roller Using a Multi-Objective Genetic Algorithm

2018 ◽  
Vol 875 ◽  
pp. 105-112 ◽  
Author(s):  
Van Quynh Le ◽  
Khac Tuan Nguyen
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Dynamic Model ◽  
Nonlinear Dynamic ◽  
Ride Comfort ◽  
Design Parameters ◽  
Nonlinear Dynamic Model ◽  
Nsga Ii ◽  
Isolation System ◽  
Multi Objective

In order to improve the vibratory roller ride comfort, a multi-objective optimization method based on the improved genetic algorithm NSGA-II is proposed to optimize the design parameters of cab’s isolation system when vehicle operates under the different conditions. To achieve this goal, 3D nonlinear dynamic model of a single drum vibratory roller was developed based on the analysis of the interaction between vibratory roller and soil. The weighted r.m.s acceleration responses of the vertical driver’s seat, pitch and roll angle of the cab are chosen as the objective functions. The optimal design parameters of cab’s isolation system are indentified based on a combination of the vehicle nonlinear dynamic model of Matlab/Simulink and the NSGA - II genetic algorithm method. The results indicate that three objective function values are reduced significantly to improve vehicle ride comfort.

scholarly journals Design of a Novel μ-Mixer

2017 ◽  
Author(s):  
Athanasios G. Kanaris ◽  
Aikaterini A. Mouza
Keyword(s):  
Reynolds Number ◽  
Pressure Drop ◽  
Swirling Flow ◽  
Numerical Data ◽  
Secondary Flows ◽  
Design Parameters ◽  
Mixing Efficiency ◽  
Geometrical Parameters ◽  
Straight Tube ◽  
Propeller Blades

In this work the efficiency of a new μ-mixer design is investigated. As in this type of devices the Reynolds number is low, mixing is diffusion dominated and it can be enhanced by creating secondary flows. In this study we propose the introduction of helical inserts into a straight tube to create swirling flow. The influence of the insert’s geometrical parameters (pitch and length of the propeller blades) and of the Reynolds number on the mixing efficiency and on the pressure drop are numerically investigated. The mixing efficiency of the device is assessed by calculating a number, i.e. the Index of Mixing Efficiency that quantifies the uniformity of concentration at the outlet of the device. The influence of the design parameters on the mixing efficiency is assessed by performing a series of “computational” experiments, in which the values of the parameter are selected using DOE methodology. Finally using the numerical data, appropriate design equations are formulated, which, for given values ​​of the design parameters, can estimate with reasonable accuracy both the mixing efficiency and the pressure drop of the proposed mixing device.

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.

Helical Pitch Optimization of Double-Tube Once-Through Steam Generator

2014 ◽  
Author(s):  
Huawei Fang ◽  
Xinyu Wei ◽  
Shoujun Yan ◽  
Jiashuang Wan ◽  
Fuyu Zhao
Keyword(s):  
Heat Transfer ◽  
Temperature Distribution ◽  
Maximum Principle ◽  
Pressure Drop ◽  
Steam Generator ◽  
Nuclear Reactor ◽  
Axial Direction ◽  
Tube Length ◽  
The Maximum Principle ◽  
Minimum Pressure

Once-Through Steam Generator (OTSG) is widely used in nuclear reactor system due to its advantages of compactness. The heat transfer performance of DOTSG is studied in this paper. In order to minimize the DOTSG volume and reduce the pressure drop of coolant, the pitch of inner helical tube is optimized with Pontryagin Maximum Principle (PMP). The double-tube is divided to three regions according to the coolant phase in secondary side. With given heat transfer load, choosing a combination function of minimum tube length and minimum pressure drop constructed with linear weighted method as objective function, the pitch optimization proceeds from superheated region to boiling region, and then to sub-cooled region in sequence, using Maximum Principle and gradient method. Then the pitch and temperature distribution along the axis is obtained respectively. The results show that the optimal pitch keeps constant along the axial direction in sub-cooled region and superheated region, but varies in boiling region. In boiling region, compared with minimum tube length optimization, the optimal tube length is 6.4% longer while the pressure drop is 36.3% smaller; and compared with minimum pressure drop optimization, the optimal pressure drop is 29.1% larger while the optimal tube length is 4.6% smaller. With the optimal pitch, the temperature distribution is in agreement with the general physic rules, which proves the correctness and the feasibility of the Maximum Principle method used for the structural optimization of DOTSG in this paper.

Performance calculation and configuration optimization of annular radiator by heat transfer unit simulation and a multi-objective genetic algorithm

2021 ◽  
pp. 095440892110017
Author(s):  
Zhe Xu ◽  
Yingqing Guo ◽  
Huarui Yang ◽  
Haotian Mao ◽  
Zongling Yu ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Pressure Drop ◽  
Design Parameters ◽  
Configuration Optimization ◽  
Nsga Ii ◽  
Transfer Unit ◽  
Side Pressure ◽  
Heat Transfer Capacity ◽  
Performance Calculation

A performance calculation method based on heat transfer unit (HTU) simulation is proposed to calculate heat transfer capacity and air-side pressure drop of Annular radiator (AR), which can avoid the problem of a huge amount of grids, and at the same time, ensure the calculation accuracy. Calculation results are compared with experimental data, and the average errors of heat transfer capacity and air-side pressure drop are 11.5%, and 5.9%, respectively, which effectively validates the effectiveness and the reliability of this method. Besides, based on HTU simulation knowledge database, a configuration optimization method of AR using Non-dominated Sorted Genetic Algorithm-II (NSGA-II) is introduced. Number of fins in circumferential direction, number of fins in axial direction, and fin height are chosen as design parameters, and two conflicting optimization objectives include heat transfer capacity maximization and air-side pressure drop minimization. Three optimal structures of AR are obtained, and the optimal results indicate that the heat transfer capacity of the optimal configurations increases by 34.31% on average compared with the original one, while the air-side pressure drop decreases by 24.00% on average, which indicates that this method is feasible and valid and can provide significant guidance for structural design of AR.

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.

CFD Analysis and Optimization of an Inlet Manifold for a Large TGU Reactor

2008 ◽  
Author(s):  
Sean McGuffie ◽  
Mike Porter
Keyword(s):  
Pressure Drop ◽  
Flow Characteristics ◽  
Flow Distribution ◽  
Complete Analysis ◽  
Design Parameters ◽  
Distribution Characteristics ◽  
Cfd Models ◽  
System Pressure ◽  
Inlet Manifold ◽  
Alternative Designs

This paper will discuss the use of Computational Fluid Dynamics (CFD) to study the flow characteristics of inlet manifolds into a large TGU reactor. The design parameters for the operation of the reactor required a very minimal system pressure drop, outside of the pressure drop across the reactor bed. For this reason several alternative designs were considered for the inlet manifolds and distribution into the reactor. Detailed CFD models were constructed of each proposed variant and analyzed to determine their pressure drop and distribution characteristics. The results of these analyses were then used to choose the best candidate for optimization as well as in providing guidance in system changes that would improve pressure drop and flow distribution characteristics. A discussion of how the results’ guidance was used in optimizing the flow path will be provided. The paper will conclude with a brief overview of other considerations in the complete analysis of the reactor system.

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