scholarly journals Preliminary Design of a Manned Nuclear Electric Propulsion Vehicle Using Genetic Algorithms

2005 ◽  
Author(s):  
Ryan W. Irwin
Keyword(s):  
Genetic Algorithms ◽  
Electric Propulsion ◽  
Preliminary Design

Using Genetic Algorithms to Set Target Values for Engineering Characteristics in the House of Quality

1999 ◽  
Author(s):  
Patricia Brackin ◽  
Jonathan Colton
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Penalty Functions ◽  
Preliminary Design ◽  
Test Results ◽  
House Of Quality ◽  
Design Specifications ◽  
Engineering Characteristics ◽  
Target Values

Abstract As part of a strategy for obtaining preliminary design specifications from the House of Quality, genetic algorithms were used to generate and optimize preliminary design specifications for an automotive case study. This paper describes the House of Quality for the automotive case study. In addition, the genetic algorithm chosen, the genetic coding, the methods used for mutation and reproduction, and the fitness and penalty functions are descrobed. Methods for determining convergence are examined. Finally, test results show that the genetic algorithm produces reasonable preliminary design specifications.

A Strategy for Extending the House of Quality to Obtain Preliminary Design Specifications

1999 ◽  
Author(s):  
Patricia Brackin ◽  
Jonathan Colton
Keyword(s):  
Genetic Algorithms ◽  
Automotive Industry ◽  
Test Case ◽  
Preliminary Design ◽  
House Of Quality ◽  
Design Specifications

Abstract This paper details a strategy for quantifying the House of Quality to produce preliminary design specifications. Estimation modules replace the traditional symbols in the HoQ. Genetic algorithms are used to produce and rate candidate preliminary design specifications. A test case from the automotive industry is implemented in order to test the effectiveness of the strategy. Results from the test case indicate that the strategy produces preliminary design specifications that are reasonable and consistent.

Preliminary Design of Hybrid-Electric Propulsion Systems for Emerging UAM Rotorcraft Architectures

2021 ◽  
Author(s):  
Chana Anna Saias ◽  
Ioannis Goulos ◽  
Ioannis Roumeliotis ◽  
Vassilios Pachidis ◽  
Marko Bacic
Keyword(s):  
Electric Propulsion ◽  
Flight Test ◽  
Preliminary Design ◽  
Propulsion Systems ◽  
Rotor Aerodynamics ◽  
Tilt Rotor ◽  
Integrated Methodology ◽  
Hybrid Electric ◽  
Battery Energy ◽  
Optimal Implementation

Abstract The increasing demands for air-taxi operations together with the ambitious targets for reduced environmental impact have driven significant interest in alternative rotorcraft architectures and propulsion systems. The design of Hybrid-Electric Propulsion Systems (HEPSs) for rotorcraft is seen as being able to contribute to those goals. This work aims to conduct a comprehensive design and trade-off analysis of hybrid powerplants for rotorcraft, targeting enhanced payload-range capability and fuel economy. An integrated methodology for the design, performance assessment and optimal implementation of HEPSs for conceptual rotorcraft has been developed. A multi-disciplinary approach is devised comprising models for rotor aerodynamics, flight dynamics, HEPS performance and weight estimation. All models are validated using experimental or flight test data. The methodology is deployed for the assessment of a hybrid-electric tilt-rotor, modelled after the NASA XV-15. This work targets to provide new insight in the preliminary design and sizing of optimally designed HEPSs for novel tilt-rotor aircraft. The paper demonstrates that at present, current battery energy densities (250Wh/kg) severely limit the degree of hybridization if a fixed useful payload and range are to be achieved. However, it is also shown that if advancements in battery energy density to 500Wh/kg are realized, a significant increase in the level of hybridization and hence reduction of fuel burned and carbon output relative to the conventional configuration can be attained. The methodology presented is flexible enough to be applied to alternative rotorcraft configurations and propulsion systems.

Optimization of Multi-Modal Discrete Functions Using Genetic Algorithms

1993 ◽  
Vol 207 (1) ◽  
pp. 53-59 ◽  
Author(s):  
D T Pham ◽  
Y Yang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problem ◽  
Approximate Solutions ◽  
Solution Set ◽  
Preliminary Design ◽  
Potential Solution ◽  
Reproduction Cycle ◽  
Fitness Sharing ◽  
Discrete Functions

Four techniques are described which can help a genetic algorithm to locate multiple approximate solutions to a multi-modal optimization problem. These techniques are: fitness sharing, ‘eliminating’ identical solutions, ‘removing’ acceptable solutions from the reproduction cycle and applying heuristics to improve sub-standard solutions. Essentially, all of these techniques operate by encouraging genetic variety in the potential solution set. The preliminary design of a gearbox is presented as an example to illustrate the effectiveness of the proposed techniques.

Using Genetic Algorithms to Set Target Values for Engineering Characteristics in the House of Quality

2002 ◽  
Vol 2 (2) ◽  
pp. 106-114 ◽  
Author(s):  
Patricia Brackin ◽  
Jonathan S. Colton
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Penalty Functions ◽  
Preliminary Design ◽  
Test Results ◽  
House Of Quality ◽  
Design Specifications ◽  
Engineering Characteristics ◽  
Target Values

As part of a strategy for obtaining preliminary design specifications from the House of Quality, genetic algorithms are used to generate and optimize preliminary design specifications for an automotive case study. This paper describes the House of Quality for an automotive case study. In addition, the genetic algorithm chosen, the genetic coding, the methods used for mutation and reproduction, and the fitness and penalty functions are described. Methods for determining convergence are examined. Finally, test results show that the genetic algorithm produces reasonable preliminary design specifications.

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