scholarly journals Optimal design of storm water inlets for highway drainage

2004 ◽  
Vol 6 (4) ◽  
pp. 245-257 ◽  
Author(s):  
John W. Nicklow ◽  
Anders P. Hellman
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Simulation Model ◽  
Drainage System ◽  
Cost Effective ◽  
Design Guidelines ◽  
Storm Water ◽  
Design Parameters ◽  
Direct Evaluation ◽  
Highway Drainage

A new methodology and computational model are developed for direct evaluation of an optimal storm water inlet design. The optimal design is defined as the least-cost combination of inlet types, sizes and locations that effectively drain a length of pavement. Costs associated with inlets are user-defined and can include those associated with materials, installation and maintenance, as well as other project-related expenses. Effective drainage is defined here as maintaining a spatial distribution of roadway spread, or top width of gutter flow, that is less than the allowable width of spread. The solution methodology is based on the coupling of a genetic algorithm and a hydraulic simulation model. The simulation model follows design guidelines established by the Federal Highway Administration and is used to implicitly solve governing hydraulic constraints that yield gutter discharge, inlet interception capacity and spread according to a design storm. The genetic algorithm is used to select the best combination of design parameters and thus solves the overall optimization problem. Capabilities of the model are successfully demonstrated through application to a hypothetical, yet realistic, highway drainage system. The example reveals that genetic algorithms and the optimal control methodology comprise a comprehensive decision-making mechanism that can be used for cost-effective design of storm water inlets and may lead to a reduced overall cost for highway drainage.

Hydrograph-based storm sewer design optimization by genetic algorithm

2006 ◽  
Vol 33 (3) ◽  
pp. 319-325 ◽  
Author(s):  
M H Afshar ◽  
A Afshar ◽  
M A Mariño ◽  
A A.S Darbandi
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Environmental Protection Agency ◽  
Optimal Solution ◽  
Storm Water ◽  
Storm Water Management Model ◽  
Decision Variables ◽  
Proposed Model ◽  
The Us ◽  
Hydraulic Simulator

A model is developed for the optimal design of storm water networks. The model uses a genetic algorithm (GA) as the search engine and the TRANSPORT module of the US Environmental Protection Agency storm water management model version 4.4H (SWMM4.4H) as the hydraulic simulator. Two different schemes are used to formulate the problem with varying degrees of success in reaching a near-optimal solution. In the first scheme, the nodal elevations and pipe diameters are selected as the decision variables of the problem which were determined by the GA to produce the trial solutions. In the second scheme, only nodal elevations are optimized by the GA, and determination of pipe diameters is left to the TRANSPORT SWMM module. Simulation of the trial solutions in both methods is carried out by the TRANSPORT module of SWMM4.4H. The proposed model is applied to some benchmark examples, and the results are presented and compared with the existing results in the literature.Key words: genetic algorithm, optimal design, sewer network, SWMM.

Optimization of Moderator Design for Explosive Detection by Thermal Neutron Activation Using a Genetic Algorithm

2016 ◽  
Vol 2 (3) ◽  
Author(s):  
Zafar Ullah Koreshi ◽  
Hamda Khan
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Neutron Activation ◽  
Thermal Neutron ◽  
Detection System ◽  
Neutron Transport ◽  
Thermal Neutron Activation ◽  
Design Parameters ◽  
Optimization Analysis ◽  
Mc Simulation

An optimal design analysis is carried out for an explosives’ detection system (EDS) based on thermal neutron activation (TNA) of a sample under investigation. The objective of this work is to use a genetic algorithm (GA) to obtain the optimized moderator design that would yield the “best” signal in a detection system. In a preliminary analysis, a full Monte Carlo (MC) simulation is carried out to estimate the effectiveness of various moderators, namely, water, graphite, and beryllium with respect to radiative capture (n,γ) reactions in a sample under investigation. Since MC simulation is computationally “expensive,” it is generally not used for random-search-based optimization analysis. Thus, more efficient methods are required for the design of optimal nuclear systems, where neutron transport is accurately modeled and iteratively solved for estimating the effect of independent design parameters. This paper proposes a computational scheme in which GA is coupled with the two-group neutron diffusion equation (DE) for carrying out an optimization analysis. The coupled GA-DE optimization scheme is demonstrated for obtaining the optimal moderator design. It is found that with considerably less computational effort than in an elaborate MC computation, the GA-DE approach can be used for the optimal design of detection systems.

Structural Design Optimization of Side/Rear Impact Guards Using a Coupled Genetic Algorithm Finite Element Method

2004 ◽  
Author(s):  
De-Shin Liu ◽  
Nan-Chun Lin ◽  
Chao-Chin Huang ◽  
Yin-Lee Meng
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Optimal Design ◽  
Computational Time ◽  
Design Parameters ◽  
Fe Model ◽  
Passenger Cars ◽  
Rear Impact ◽  
Structural Design Optimization ◽  
Energy Absorbing

Underride protective structure can reduce serious injures when passenger cars collide with the rear end or side of the heavy vehicle. This paper describes the use of Genetic Algorithm (GA) coupled with a dynamic, inelastic and large deformation finite element (FE) code LS-DYNA to search optimal design of the Side/Rear impact guards. In order to verify the accuracy of the FE model, the simulation results were compared with real experiments follow with the regulation ECE R73. The validated FE model then used to study the optimal design base on under running distance and total amount of energy absorbing capacity. The results from this study shown that this newly developed method not only can found multi-objective design parameters but also can reduce computational time significantly.

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.

Study on Regional Traffic Control Optimization Based on State Analysis

2010 ◽  
Vol 44-47 ◽  
pp. 3959-3964
Author(s):  
Liang Zhi Zhang ◽  
Lei Jia ◽  
Mi Nai He
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Traffic Control ◽  
User Equilibrium ◽  
Solution Procedure ◽  
Design Parameters ◽  
Signal Control ◽  
Control Optimization ◽  
Proposed Model ◽  
Control Scheme

The aim of regional traffic control optimization is to find the optimal design parameters while thinking over the route choice of users. This problem can be formulated as a bi-level programming program. In the program, signal control scheme and user equilibrium traffic assignment are optimized in the upper and lower level respectively. The solution procedure developed with the genetic algorithm has been tested with an example of factual road network.Numerical experiment verified the proposed model is quite promising for use in design of regional signal control.

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.

scholarly journals Integrated inverse design of ventilation for an aircraft cabin

2019 ◽  
Vol 85 ◽  
pp. 05006
Author(s):  
Yun Wei ◽  
Wei Liu ◽  
Yu Xue ◽  
Zhiqiang John Zhai ◽  
Qingyan Yan Chen ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Adjoint Method ◽  
Design Criterion ◽  
Fine Tuning ◽  
Design Stage ◽  
Design Parameters ◽  
Integrated Method ◽  
Aircraft Cabin ◽  
Air Supply

Cabin ventilation is crucial for maintaining thermal comfort and air quality for passengers and crew. The genetic algorithm, proper orthogonal decomposition (POD), and adjoint method have been proposed to inversely design the cabin ventilation. However, each method has its cons and pros. This paper proposed to integrate the above three methods in cascades. The genetic algorithm was applied first in the first stage to roughly circumscribe the ranges of design parameters. Then POD was applied in the next stage to further narrow the ranges and estimate the optimal parametric sets for each design criterion. The estimated optimal design from POD was supplied to the adjoint method for fine tuning. The air-supply parameters of a five-row aircraft cabin were inversely designed to achieve the minimum absolute value of the predicted mean vote (PMV) and the minimum averaged mean age of air. The results showed that the integrated method was able to improve the design stage by stage. The integrated method has superior advantages to assure the optimal design while minimizing the computing expense.

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.

OPTIMAL GEOMETRICAL DESIGN OF AIRCRAFT USING GENETIC ALGORITHMS

2003 ◽  
Vol 26 (4) ◽  
pp. 373-388 ◽  
Author(s):  
Nicholas Ali ◽  
Kamran Behdinan
Keyword(s):  
Genetic Algorithm ◽  
Penalty Method ◽  
Aircraft Design ◽  
Cost Effective ◽  
Design Parameters ◽  
Adaptive Penalty ◽  
Search Tool ◽  
And Performance ◽  
Aircraft Sizing ◽  
Adaptive Penalty Method

With the advent of computers and search and optimization tools such as the genetic algorithm, the ability to manipulate numerous aircraft design parameters in a reasonable amount of time is feasible. From this angle, the lengthy time and effort spent creating and integrating aerodynamics codes, sizing routines, and performance modules, can be mitigated by the use of a genetic algorithm. Consequently, a genetic algorithm has been created and employed as a cost effective tool to explore possible aircraft geometries in the conceptual design process of the aircraft. A program has been developed to address most aspects of aircraft design such as aircraft sizing and configuration, performance, and propulsion, to name a few. These codes have been integrated into a genetic algorithm, which performs the task of searching and optimizing. The adaptive penalty method has been employed to handle all constraints imposed on the design. In addition, adjustments for varying degrees of selection and crossover intensities and types have been studied. A design study has also been carried out to compare an existing aircraft shape with the genetic algorithm optimized aircraft shape and configuration. Results indicate that the genetic algorithm is a powerful multi-disciplinary optimization and search tool, capable of simultaneously managing and varying numerous aircraft design parameters. Moreover, the genetic algorithm is capable of finding aircraft geometries and configurations that are both efficient and cost effective.

MULTIOBJECTIVE OPTIMIZATION OF PROCESS PLANT USING GENETIC ALGORITHM

2005 ◽  
Vol 05 (04) ◽  
pp. 425-437 ◽  
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.

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