scholarly journals Optimization Algorithms for Aircraft Preliminary Sizing and Cabin Design

2021 ◽  
Author(s):  
Joseph Yang ◽  
Mihaela Niță ◽  
Dieter Scholz
Keyword(s):  
Monte Carlo ◽  
Simulated Annealing ◽  
Optimization Algorithms ◽  
Small Error ◽  
Steepest Descent ◽  
Design Parameters ◽  
Stochastic Methods ◽  
Trial And Error ◽  
Gaussian Random Walk ◽  
Fuel Costs

As new aircraft are being designed, optimization of the design parameters becomes necessary to decrease fuel costs and emissions and maximize profits. As opposed to trial-and-error where a design may go through several rounds of testing to improve efficiency, optimization algorithms can save time and effort when implemented properly. Optimization algorithms are of two types: stochastic and deterministic. The stochastic methods used are: Random Monte Carlo, Gaussian Random Walk, and Simulated Annealing. The deterministic method examined is the method of Orthogonal Steepest Descent (OSD). Orthogonal Steepest Descent seems to be the fastest method which is also quite accurate. The next fastest method is Simulated Annealing. The Random Monte Carlo method is less precise by nature, and experiences a greater error and time elapsed because it requires many more iterations to arrive at reasonably small error.

Gear Design Optimization Algorithms: A Review

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
YO Usman ◽  
PO Odion ◽  
EO Onibere ◽  
AY Egwoh
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Design Optimization ◽  
Ant Colony Optimization ◽  
Optimization Algorithms ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Design Parameters ◽  
Gear Design ◽  
Complex Design

Gearing is one of the most efficient methods of transmitting power from a source to its application with or without change of speed or direction. Gears are round mechanical components with teeth arranged in their perimeter. Gear design is complex design that involves many design parameters and tables, finding an optimal or near optimal solution to this complex design is still a major challenge. Different optimization algorithms such as Genetic Algorithm (GA), Simulated Annealing, Ant-Colony Optimization, and Neural Network etc., have been used for design optimization of the gear design problems. This paper focuses on the review of the optimization techniques used for gear design optimization with a view to identifying the best of them. Nowadays, the method used for the design optimization of gears is the evolutionary algorithm specifically the genetic algorithm which is based on the evolution idea of natural selection. The study revealed that GA. has the ability to find optimal solutions in a short time of computation by making a global search in a large search space. Keywords: Firefly Algorithm, Ant-Colony Optimization, Simulated Annealing, Genetic Algorithm, Gear design, Optimization, Particle Swarm Optimization Algorithm

Nappes d'huile: prediction de leurs deplacements comme agent efficace d'un plan de mesure d'urgence

1979 ◽  
Vol 14 (1) ◽  
pp. 89-109
Author(s):  
B. Coupal ◽  
M. de Broissia
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Finite Elements ◽  
Mass Balance ◽  
Stochastic Method ◽  
Stochastic Methods ◽  
Deterministic Approach ◽  
First Order ◽  
The Third ◽  
Comme Agent

Abstract The movement of oil slicks on open waters has been predicted, using both deterministic and stochastic methods. The first method, named slick rose, consists in locating an area specifying the position of the slick during the first hours after the spill. The second method combines a deterministic approach for the simulation of current parameters to a stochastic method simulating the wind parameters. A Markov chain of the first order followed by a Monte Carlo approach enables the simulation of both phenomena. The third method presented in this paper describes a mass balance on the spilt oil, solved by the method of finite elements. The three methods are complementary to each other and constitute an important point for a contingency plan.

Velocity log upscaling based on reversible jump Markov chain Monte Carlo simulated annealing

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. R293-R305 ◽  
Author(s):  
Sireesh Dadi ◽  
Richard Gibson ◽  
Kainan Wang
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Simulated Annealing ◽  
Markov Chain Monte Carlo ◽  
Random Perturbation ◽  
Current Model ◽  
Sampling Technique ◽  
Maximum Deviation ◽  
Reversible Jump ◽  
Layer Boundary

Upscaling log measurements acquired at high frequencies and correlating them with corresponding low-frequency values from surface seismic and vertical seismic profile data is a challenging task. We have applied a sampling technique called the reversible jump Markov chain Monte Carlo (RJMCMC) method to this problem. A key property of our approach is that it treats the number of unknowns itself as a parameter to be determined. Specifically, we have considered upscaling as an inverse problem in which we considered the number of coarse layers, layer boundary depths, and material properties as the unknowns. The method applies Bayesian inversion, with RJMCMC sampling and uses simulated annealing to guide the optimization. At each iteration, the algorithm will randomly move a boundary in the current model, add a new boundary, or delete an existing boundary. In each case, a random perturbation is applied to Backus-average values. We have developed examples showing that the mismatch between seismograms computed from the upscaled model and log velocities improves by 89% compared to the case in which the algorithm is allowed to move boundaries only. The layer boundary distributions after running the RJMCMC algorithm can represent sharp and gradual changes in lithology. The maximum deviation of upscaled velocities from Backus-average values is less than 10% with most of the values close to zero.

Geometric Optimization of Concentrating Solar Collectors using Monte Carlo Simulation

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
A. J. Marston ◽  
K. J. Daun ◽  
M. R. Collins
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Stochastic Programming ◽  
Objective Function ◽  
Sample Size ◽  
Optimization Algorithm ◽  
Geometric Optimization ◽  
Trial And Error ◽  
Solar Collectors ◽  
Step Size

This paper presents an optimization algorithm for designing linear concentrating solar collectors using stochastic programming. A Monte Carlo technique is used to quantify the performance of the collector design in terms of an objective function, which is then minimized using a modified Kiefer–Wolfowitz algorithm that uses sample size and step size controls. This process is more efficient than traditional “trial-and-error” methods and can be applied more generally than techniques based on geometric optics. The method is validated through application to the design of three different configurations of linear concentrating collector.

Optimization by parameter-perturbation correlation

SIMULATION ◽  
1968 ◽  
Vol 11 (2) ◽  
pp. 57-60
Author(s):  
Robert Gonzalez ◽  
Milo Muterspaugh
Keyword(s):  
Analog Computer ◽  
Steepest Descent ◽  
Design Parameters ◽  
Model Matching ◽  
State Variables ◽  
Parameter Perturbation ◽  
Simple Method ◽  
Matching Problems ◽  
Functional Value ◽  
Term Paper

This paper describes a simple method of steepest-descent optimization of a criterion functional F(α1, α2,...) depend ing on the state variables y i(t;α1,α2,...) of a dynamic system with design parameters α 1, α 2.... The system is simulated on a fast analog computer and the parameters are given mutually orthogonal sequences of binary pertur bations during successive computer runs. Simple correla tion of each parameter perturbation with the criterion functional value at the end of each computer run yields ap proximations for the gradient components ∂ F/∂α k needed for steepest-descent optimization with a minimum- of dig ital logic. As an example, two- and three-parameter model- matching problems are solved by iterative computation at 1000 iterations per second. A course term-paper for first-year graduate students

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