Ramjet Powered Missile Design Using a Genetic Algorithm

2007 ◽  
Vol 7 (2) ◽  
pp. 167-173 ◽  
Author(s):  
Roy J. Hartfield ◽  
Rhonald M. Jenkins ◽  
John E. Burkhalter
Keyword(s):  
Genetic Algorithm ◽  
Performance Prediction ◽  
Design Space ◽  
System Optimization ◽  
Design Strategy ◽  
Optimization Process ◽  
Preliminary Design ◽  
Efficient Exploration ◽  
Typical Design ◽  
Design Scenario

A methodology for developing optimized designs for symmetric-centerbody ramjet powered missiles, using a genetic algorithm as the driver for the system optimization process, has been developed. The methodology described in this paper allows for a comprehensive but efficient exploration of the design space. This global optimization process is made possible by performance prediction codes, which can provide preliminary design-level accuracy very efficiently. This work demonstrates the first truly comprehensive design strategy for this type of device. The paper contains a discussion of the methodology and shows results for a typical design scenario.

A Novel Tool Design Strategy for Electromagnetic Forming

2014 ◽  
Vol 1018 ◽  
pp. 333-340 ◽  
Author(s):  
Reimund Neugebauer ◽  
Verena Psyk ◽  
Christian Scheffler
Keyword(s):  
Design Strategy ◽  
Tool Design ◽  
Electromagnetic Forming ◽  
Optimization Process ◽  
Magnetic Pulse ◽  
Preliminary Design ◽  
Magnetic Pulse Welding ◽  
Iterative Optimization ◽  
Industrial Manufacturing ◽  
Pulse Welding

To make the advantages of electromagnetic forming applicable for industrial manufacturing, a three step tool design strategy is suggested. At first, simplified decoupled electromagnetic and structural mechanical simulations are used for creating a preliminary design via a systematic iterative optimization process. The selected design is verified in more accurate coupled simulations. A prototypic realization serves for further optimization, if necessary. The applicability of the approach is proved on the basis of an inductor system for magnetic pulse welding of tubes.

Modeling Noise in Approximation-Based Robust Design: A Comparison and Critical Discussion

1998 ◽  
Author(s):  
Patrick N. Koch ◽  
Dimitri Mavris ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Robust Design ◽  
Design Space ◽  
Thermodynamic Cycle ◽  
Critical Discussion ◽  
Preliminary Design ◽  
Robust Solutions ◽  
Modeling Approach ◽  
Predicting Performance ◽  
Performance Variation ◽  
Efficient Exploration

Abstract Robust design techniques are being developed that incorporate computational experimentation and approximation for efficient exploration of a preliminary design space. Critical to such approximation-based robust design approaches is the effective modeling of the effects of noise (uncontrollable factors) on performance (measuring and modeling performance variation). Given that approximations are sought for efficient exploration, the question addressed in this paper is the following: How can noise be modeled and incorporated effectively into the preliminary design of complex systems to effectively identify robust solutions? Three approaches for modeling noise when approximate performance models are sought are tested and compared in this paper: statistical expected value and Taylor’s expansion, design of experiments (DOE)-based Monte Carlo simulation, and product arrays. The focus in this testing and comparison is on modeling and predicting performance variation. The example problem employed is the preliminary design of the thermodynamic cycle for a commercial turbofan turbine engine. The approximate models constructed for this example using each modeling approach are employed for exploring the preliminary design space and identifying robust solutions. The robust solutions obtained are compared and a discussion of the appropriate uses of each modeling approach is presented.

A modified genetic algorithm for system optimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alexander Zemliak
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
System Optimization ◽  
High Accuracy ◽  
Optimization Process ◽  
Processing Unit ◽  
Local Minima ◽  
Control Vector ◽  
Content Type ◽  
Cpu Time

Purpose In this paper, the previously developed idea of generalized optimization of circuits for deterministic methods has been extended to genetic algorithm (GA) to demonstrate new possibilities for solving an optimization problem that enhance accuracy and significantly reduce computing time. Design/methodology/approach The disadvantages of GAs are premature convergence to local minima and an increase in the computer operation time when setting a sufficiently high accuracy for obtaining the minimum. The idea of generalized optimization of circuits, previously developed for the methods of deterministic optimization, is built into the GA and allows one to implement various optimization strategies based on GA. The shape of the fitness function, as well as the length and structure of the chromosomes, is determined by a control vector artificially introduced within the framework of generalized optimization. This study found that changing the control vector that determines the method for calculating the fitness function makes it possible to bypass local minima and find the global minimum with high accuracy and a significant reduction in central processing unit (CPU) time. Findings The structure of the control vector is found, which makes it possible to reduce the CPU time by several orders of magnitude and increase the accuracy of the optimization process compared with the traditional approach for GAs. Originality/value It was demonstrated that incorporating the idea of generalized optimization into the body of a stochastic optimization method leads to qualitatively new properties of the optimization process, increasing the accuracy and minimizing the CPU time.

scholarly journals Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Jai Hoon Park ◽  
Kang Hoon Lee
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Design Space ◽  
Learning Algorithm ◽  
Computational Design ◽  
Computational Method ◽  
Learning Ability ◽  
Modular Robots ◽  
Control Mechanisms ◽  
Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

Actual Design Space Methodology for Preliminary Design Analysis of Switched Reluctance Machines

2021 ◽  
Vol 57 (1) ◽  
pp. 397-408
Author(s):  
Roberto Rocca ◽  
Fabio Giulii Capponi ◽  
Giulio De Donato ◽  
Savvas Papadopoulos ◽  
Federico Caricchi ◽  
...  
Keyword(s):  
Design Space ◽  
Design Analysis ◽  
Preliminary Design ◽  
Switched Reluctance ◽  
Switched Reluctance Machines ◽  
Actual Design

Wave Energy Converter Array Optimization: A Review of Current Work and Preliminary Results of a Genetic Algorithm Approach Introducing Cost Factors

2015 ◽  
Author(s):  
Chris Sharp ◽  
Bryony DuPont
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Wave Energy ◽  
Reference Model ◽  
A Priori ◽  
System Optimization ◽  
Primary Energy ◽  
Close Relative ◽  
Cost Information ◽  
Multiple Test

Currently, ocean wave energy is a novel means of electricity generation that is projected to potentially serve as a primary energy source in coastal areas. However, for wave energy converters (WECs) to be applicable on a scale that allows for grid implementation, these devices will need to be placed in close relative proximity to each other. From what’s been learned in the wind industry of the U.S., the placement of these devices will require optimization considering both cost and power. However, current research regarding optimized WEC layouts only considers the power produced. This work explores the development of a genetic algorithm (GA) that will create optimized WEC layouts where the objective function considers both the economics involved in the array’s development as well as the power generated. The WEC optimization algorithm enables the user to either constrain the number of WECs to be included in the array, or allow the algorithm to define this number. To calculate the objective function, potential arrays are evaluated using cost information from Sandia National Labs Reference Model Project, and power development is calculated such that WEC interaction affects are considered. Results are presented for multiple test scenarios and are compared to previous literature, and the implications of a priori system optimization for offshore renewables are discussed.

Optimum Selection of Hydraulic Devices for Water Hammer Control in the Pipeline Systems Using Genetic Algorithm

2003 ◽  
Author(s):  
Bong Seong Jung ◽  
Bryan W. Karney
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Optimization Problems ◽  
System Optimization ◽  
Water Hammer ◽  
Pipeline System ◽  
Optimal Size ◽  
Protection Strategy ◽  
Hydraulic Devices ◽  
Selection Of

Genetic algorithms have been used to solve many water distribution system optimization problems, but have generally been limited to steady state or quasi-steady state optimization. However, transient events within pipe system are inevitable and the effect of water hammer should not be overlooked. The purpose of this paper is to optimize the selection, sizing and placement of hydraulic devices in a pipeline system considering its transient response. A global optimal solution using genetic algorithm suggests optimal size, location and number of hydraulic devices to cope with water hammer. This study shows that the integration of a genetic algorithm code with a transient simulator can improve both the design and the response of a pipe network. This study also shows that the selection of optimum protection strategy is an integrated problem, involving consideration of loading condition, device and system characteristics, and protection strategy. Simpler transient control systems are often found to outperform more complex ones.

Comparison of Piston Concept Design Solutions for Composite Cycle Engines Part II: Design Considerations

2021 ◽  
Author(s):  
Dimitrios Chatzianagnostou ◽  
Stephan Staudacher
Keyword(s):  
Gas Turbine ◽  
Design Space ◽  
Piston Compressor ◽  
Design System ◽  
Surface Load ◽  
Preliminary Design ◽  
Concept Design ◽  
Piston Engine ◽  
Engine Design ◽  
Cylinder Piston

Abstract Hecto pressure composite cycle engines with piston engines and piston compressors are potential alternatives to advanced gas turbine engines. The nondimensional groups limiting their design have been introduced and generally discussed in Part I [1]. Further discussion shows, that the ratio of effective power to piston surface characterizes the piston thermal surface load capability. The piston design and the piston cooling technology level limit its range of values. Reynolds number and the required ratio of advective to diffusive material transport limit the stroke-to-bore ratio. Torsional frequency sets a limit to crankshaft length and hence cylinder number. A rule based preliminary design system for composite cycle engines is presented. Its piston engine design part is validated against data of existing piston engines. It is used to explore the design space of piston components. The piston engine design space is limited by mechanical feasibility and the crankshaft overlap resulting in a minimum stroke-to-bore ratio. An empirical limitation on stroke-to-bore ratio is based on existing piston engine designs. It limits the design space further. Piston compressor design does not limit the piston engine design but is strongly linked to it. The preliminary design system is applied to a composite cycle engines of 22MW take-off shaft power, flying a 1000km mission. It features three 12-cylinder piston engines and three 20-cylinder piston compressors. Its specific fuel consumption and mission fuel burn are compared to an intercooled gas turbine with pressure gain combustion of similar technology readiness.

scholarly journals Design Space Approach for Preservative System Optimization of an Anti-Aging Eye Fluid Emulsion

2015 ◽  
Vol 18 (3) ◽  
pp. 551 ◽  
Author(s):  
Felipe Rebello Lourenço ◽  
Fabiane Lacerda Francisco ◽  
Márcia Regina Spuri Ferreira ◽  
Terezinha De Jesus Andreoli ◽  
Raimar Löbenberg ◽  
...  
Keyword(s):  
Design Space ◽  
Challenge Test ◽  
System Optimization ◽  
Effective Concentration ◽  
Optimization Strategy ◽  
Cosmetic Products ◽  
Cosmetic Product ◽  
Space Design ◽  
New Formula ◽  
Space Approach

The use of preservatives must be optimized in order to ensure the efficacy of an antimicrobial system as well as the product safety. Despite the wide variety of preservatives, the synergistic or antagonistic effects of their combinations are not well established and it is still an issue in the development of pharmaceutical and cosmetic products. The purpose of this paper was to establish a space design using a simplex-centroid approach to achieve the lowest effective concentration of 3 preservatives (methylparaben, propylparaben, and imidazolidinyl urea) and EDTA for an emulsion cosmetic product. Twenty-two formulae of emulsion differing only by imidazolidinyl urea (A: 0.00 to 0.30% w/w), methylparaben (B: 0.00 to 0.20% w/w), propylparaben (C: 0.00 to 0.10% w/w) and EDTA (D: 0.00 to 0.10% w/w) concentrations were prepared. They were tested alone and in binary, ternary and quaternary combinations. Aliquots of these formulae were inoculated with several microorganisms. An electrochemical method was used to determine microbial burden immediately after inoculation and after 2, 4, 8, 12, 24, 48, and 168 h. An optimization strategy was used to obtain the concentrations of preservatives and EDTA resulting in a most effective preservative system of all microorganisms simultaneously. The use of preservatives and EDTA in combination has the advantage of exhibiting a potential synergistic effect against a wider spectrum of microorganisms. Based on graphic and optimization strategies, we proposed a new formula containing a quaternary combination (A: 55%; B: 30%; C: 5% and D: 10% w/w), which complies with the specification of a conventional challenge test. A design space approach was successfully employed in the optimization of concentrations of preservatives and EDTA in an emulsion cosmetic product. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.

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