Hydro-aerodynamic mathematical model and multi-objective optimization of wing-in-ground effect craft in take-off

2017 ◽  
Vol 232 (4) ◽  
pp. 421-433 ◽  
Author(s):  
Mohammad Tavakoli Dakhrabadi ◽  
Mohammad Saeed Seif
Keyword(s):  
Mathematical Model ◽  
High Speed ◽  
Ground Effect ◽  
Practical Method ◽  
Aerodynamic Characteristics ◽  
Center Of Gravity ◽  
Computational Time ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Wing In Ground Effect

Hydro-aerodynamic mathematical model and multi-objective optimization of a popular wing-in-ground effect craft are presented in this research using a hydro-aerodynamic practical method and the genetic algorithm. The primary components of the wing-in-ground effect craft configuration include a compound wing, catamaran hull form and a power-augmented ram platform. The hydro-aerodynamic practical method with low computational time and high accuracy is performed by coupling hydrodynamic and aerodynamic considerations using the potential flow theory in ground effect and the semi-empirical equations proposed for high-speed marine vehicles. The trade-off between hydrodynamic and aerodynamic characteristics makes it difficult to simultaneously satisfy the design requirements of high hydro-aerodynamic performance. In this article, three goals—reduced hump resistance, increased compound wing lift-to-drag ratio and reduced take-off speed—are selected as the objective functions. The longitudinal position of center of gravity, position of outer wing with respect to main wing, power augmented ram platform angle to horizontal and flap angle are also adopted as design variables. Static height stability and the location of the center of gravity with respect to the aerodynamics centers are considered as constraints for the stable flight in ground effect. The optimal solutions of the multi-objective optimization were not unique, rather a set of non-dominated optima, called the Pareto sets, are obtained. As a result of the multi-objective optimization, 25 Pareto individuals are obtained that the naval architects can use in designing wing-in-ground crafts.

Multi-Objective Pricing Optimization for a High-Speed Rail Network Under Competition

2019 ◽  
Vol 2673 (7) ◽  
pp. 215-226 ◽  
Author(s):  
Huizhuo Cao ◽  
Xuemei Li ◽  
Vikrant Vaze ◽  
Xueyan Li
Keyword(s):  
High Speed ◽  
Programming Model ◽  
Pareto Frontier ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
High Speed Rail ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Single Objective

Multi-objective pricing of high-speed rail (HSR) passenger fares becomes a challenge when the HSR operator needs to deal with multiple conflicting objectives. Although many studies have tackled the challenge of calculating the optimal fares over railway networks, none of them focused on characterizing the trade-offs between multiple objectives under multi-modal competition. We formulate the multi-objective HSR fare optimization problem over a linear network by introducing the epsilon-constraint method within a bi-level programming model and develop an iterative algorithm to solve this model. This is the first HSR pricing study to use an epsilon-constraint methodology. We obtain two single-objective solutions and four multi-objective solutions and compare them on a variety of metrics. We also derive the Pareto frontier between the objectives of profit and passenger welfare to enable the operator to choose the best trade-off. Our results based on computational experiments with Beijing–Shanghai regional network provide several new insights. First, we find that small changes in fares can lead to a significant improvement in passenger welfare with no reduction in profitability under multi-objective optimization. Second, multi-objective optimization solutions show considerable improvements over the single-objective optimization solutions. Third, Pareto frontier enables decision-makers to make more informed decisions about choosing the best trade-offs. Overall, the explicit modeling of multiple objectives leads to better pricing solutions, which have the potential to guide pricing decisions for the HSR operators.

scholarly journals Methodology to Solve the Multi-Objective Optimization of Acrylic Acid Production Using Neural Networks as Meta-Models

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1184
Author(s):  
Geraldine Cáceres Sepulveda ◽  
Silvia Ochoa ◽  
Jules Thibault
Keyword(s):  
Neural Networks ◽  
Acrylic Acid ◽  
Performance Criteria ◽  
Computational Time ◽  
Multi Objective Optimization ◽  
Meta Model ◽  
Multi Objective ◽  
Pareto Domain ◽  
Computationally Intensive ◽  
Time Required

It is paramount to optimize the performance of a chemical process in order to maximize its yield and productivity and to minimize the production cost and the environmental impact. The various objectives in optimization are often in conflict, and one must determine the best compromise solution usually using a representative model of the process. However, solving first-principle models can be a computationally intensive problem, thus making model-based multi-objective optimization (MOO) a time-consuming task. In this work, a methodology to perform the multi-objective optimization for a two-reactor system for the production of acrylic acid, using artificial neural networks (ANNs) as meta-models, is proposed in an effort to reduce the computational time required to circumscribe the Pareto domain. The performance of the meta-model confirmed good agreement between the experimental data and the model-predicted values of the existent relationships between the eight decision variables and the nine performance criteria of the process. Once the meta-model was built, the Pareto domain was circumscribed based on a genetic algorithm (GA) and ranked with the net flow method (NFM). Using the ANN surrogate model, the optimization time decreased by a factor of 15.5.

Multi-Objective Optimization Design of Screw Conveyor Using Genetic Algorithm

2013 ◽  
Vol 732-733 ◽  
pp. 402-406
Author(s):  
Duan Yi Wang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimization Design ◽  
Optimization Problem ◽  
Machine Design ◽  
Screw Conveyor ◽  
Optimized Design ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
The Mathematical Model

The weight minimum and drive efficiency maxima1 of screw conveyor were considered as double optimizing objects in this paper. The mathematical model of the screw conveyor has been established based on the theory of the machine design, and the genetic algorithm was adopted to solving the multi-objective optimization problem. The results show that the mass of spiral shaft reduces 13.6 percent, and the drive efficiency increases 6.4 percent because of the optimal design based on genetic algorithm. The genetic algorithm application on the screw conveyor optimized design can provided the basis for designing the screw conveyor.

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