scholarly journals Identification of a Non-Linear Landing Gear Model Using Nature-Inspired Optimization

2008 ◽  
Vol 15 (3-4) ◽  
pp. 257-272 ◽  
Author(s):  
Felipe A.C. Viana ◽  
Valder Steffen Jr. ◽  
Marcelo A.X. Zanini ◽  
Sandro A. Magalhães ◽  
Luiz C.S. Góes
Keyword(s):  
Inverse Problem ◽  
Shock Absorber ◽  
Model Updating ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Initial Guess ◽  
Landing Gear ◽  
Design Variables ◽  
Non Linear ◽  
The Difference

This work deals with the application of a nature-inspired optimization technique to solve an inverse problem represented by the identification of an aircraft landing gear model. The model is described in terms of the landing gear geometry, internal volumes and areas, shock absorber travel, tire type, and gas and oil characteristics of the shock absorber. The solution to this inverse problem can be obtained by using classical gradient-based optimization methods. However, this is a difficult task due to the existence of local minima in the design space and the requirement of an initial guess. These aspects have motivated the authors to explore a nature-inspired approach using a method known as LifeCycle Model. In the present formulation two nature-based methods, namely the Genetic Algorithms and the Particle Swarm Optimization were used. An optimization problem is formulated in which the objective function represents the difference between the measured characteristics of the system and its model counterpart. The polytropic coefficient of the gas and the damping parameter of the shock absorber are assumed as being unknown: they are considered as design variables. As an illustration, experimental drop test data, obtained under zero horizontal speed, were used in the non-linear landing gear model updating of a small aircraft.

Design Optimization of Valveless DTH Pneumatic Hammers by a Weighted Pseudo-Gradient Search Method

1998 ◽  
Vol 120 (4) ◽  
pp. 687-694 ◽  
Author(s):  
L. E. Chiang ◽  
E. B. Stamm
Keyword(s):  
Dynamic Model ◽  
Linear Equations ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Variables ◽  
Standard Procedures ◽  
Non Linear ◽  
Predicted Values ◽  
Gradient Search Method ◽  
Non Linear Equations

A design methodology for Down-The-Hole (DTH) pneumatic hammers used for rock drilling is proposed which renders an optimal design for a given set of constraints. A generic non-linear dynamic model developed by the authors is used to compute the hammer performance. This model consists of a set of six differential equations plus a set of twenty non-linear polynomial equations. In addition there are parameter range restrictions given by fabrication and operational standard procedures. In any given application, magnitudes such as power, impact energy, frequency, efficiency and mass flow may be sought for optimality. However these magnitudes must be computed by integration after solving the dynamic model over an entire cycle, thus traditional optimization methods for non-linear equations that are based in gradient information are not suitable. Hence a method that uses secant information is used to approximate the gradient of the space of design variables. Several prototypes using this optimization method have been designed and field tested. The results are in agreement with predicted values.

Selection of Body Segment Parameters by Optimization Methods

1982 ◽  
Vol 104 (1) ◽  
pp. 38-44 ◽  
Author(s):  
C. L. Vaughan ◽  
J. G. Andrews ◽  
J. G. Hay
Keyword(s):  
Optimization Theory ◽  
Bilateral Symmetry ◽  
Optimization Methods ◽  
Body Segment ◽  
Kinematic Data ◽  
Design Variables ◽  
The Difference ◽  
Body Segment Parameters ◽  
Gamma Scanning ◽  
Selection Of

The selection of body segment parameters (BSPs) is a difficult yet essential task in many biomechanical studies. The methods used to date—cadaver, reaction board, mathematical modeling, gamma scanning, and kinematics—all have a number of drawbacks. The purpose of the present paper is to present an alternative method, based on kinematic data and optimization theory, for selecting BSPs. The design variables are the BSPs and the objective function to be minimized is based on the difference between calculated and measured distal extremity kinetics, while the equality constraints are based on Newtonian principles as well as bilateral symmetry of the BSPs. Three different activities are used to generate “optimal” sets of BSPs and these values are different, but not markedly so, from cadaver values. Further detailed investigation appears warranted.

An Improved Taguchi Method and its Application in Finite Element Model Updating of Bridges

2010 ◽  
Vol 456 ◽  
pp. 51-65 ◽  
Author(s):  
Hang Sun ◽  
Yang Liu
Keyword(s):  
Taguchi Method ◽  
Structural Model ◽  
Model Updating ◽  
Optimization Technique ◽  
Element Model ◽  
Good Choice ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
The Difference ◽  
Low Efficiency

In the model updating process, the objective function is usually set as the weighted sum of the difference between analytical and experimental dynamic characteristics. But it is difficult to select the weighting factors since the relative importance of each parameter to updated results is not obvious but specific for different problem. To overcome this problem, multi-objective genetic algorithm (GA) is introduced into model updating by Gyeong-Ho Kim since there is no need for selecting weighting values in multi-objective optimization technique. To complex structures, however, it is difficult to update the structural models by GA because of the relative low efficiency. While Taguchi updating method, deemed as an efficient and robust method, is a good choice to update the models of large structures. But Taguchi method is only applied to solve the single objective optimization problem of model updating. Therefore, this paper proposed improved Taguchi updating method to deal with the problem of model updating using multi-objective optimization technique. Then the proposed method is applied to update the model of a 14-bay beam with measured frequencies and modal shapes. The updated results show that the proposed method is promising to structural model updating.

Truncation Error Based Mesh Optimization

2020 ◽  
Vol 5 (4) ◽  
Author(s):  
Charles W. Jackson ◽  
Christopher J. Roy ◽  
Christopher R. Schrock
Keyword(s):  
Truncation Error ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Discretization Error ◽  
Two Dimensions ◽  
Optimization Techniques ◽  
Local Source ◽  
Mesh Optimization ◽  
Design Variables ◽  
Mesh Equidistribution

Abstract Truncation error is used to drive mesh adaptation in order to reduce the discretization error in solutions to a variety of 1D and 2D flow problems. The adaptation is performed using r-adaptation to move the mesh nodes in the domain in an attempt to reduce the truncation error since it is the local source of discretization error. Here, we present a new set of r-adaptation methods called mesh optimization along with three different ways of performing this type of adaptation. Each of these techniques uses a finite difference gradient-based local optimization technique with different sets of design variables to create a mesh that minimizes a functional based on truncation error. These new truncation error based mesh optimization techniques are compared to a more common truncation error based mesh equidistribution technique. Some observations on the performance and behavior of the different adaptation methods and best practices for their use are presented. All of the optimization methods are shown to reduce the truncation error one or two orders of magnitude and the discretization error by roughly one order of magnitude for the 1D problems tested. In two dimensions, the optimization-based adaptation methods are able to reduce the discretization error by up to a factor of seven. Mesh equidistribution achieved similar levels of improvement for much less cost compared to the mesh optimization techniques.

NON-LINEAR GLOBAL OPTIMIZATION VIA PARAMETERIZATION AND INVERSE FUNCTION APPROXIMATION: AN ARTIFICIAL NEURAL NETWORKS APPROACH

2007 ◽  
Vol 17 (05) ◽  
pp. 353-368 ◽  
Author(s):  
RENÉ V. MAYORGA ◽  
MARIANO ARRIAGA
Keyword(s):  
Global Optimization ◽  
Optimization Technique ◽  
Inverse Function ◽  
Optimization Methods ◽  
Computational Effort ◽  
Linear Functions ◽  
Local Minima ◽  
Non Linear ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this article, a novel technique for non-linear global optimization is presented. The main goal is to find the optimal global solution of non-linear problems avoiding sub-optimal local solutions or inflection points. The proposed technique is based on a two steps concept: properly keep decreasing the value of the objective function, and calculating the corresponding independent variables by approximating its inverse function. The decreasing process can continue even after reaching local minima and, in general, the algorithm stops when converging to solutions near the global minimum. The implementation of the proposed technique by conventional numerical methods may require a considerable computational effort on the approximation of the inverse function. Thus, here a novel Artificial Neural Network (ANN) approach is implemented to reduce the computational requirements of the proposed optimization technique. This approach is successfully tested on some highly non-linear functions possessing several local minima. The results obtained demonstrate that the proposed approach compares favorably over some current conventional numerical (Matlab functions) methods, and other non-conventional (Evolutionary Algorithms, Simulated Annealing) optimization methods.

A Literature Review: Solving Constrained Non-Linear Bi-Level Optimization Problems With Classical Methods

2019 ◽  
Author(s):  
Arpan Biswas ◽  
Christopher Hoyle
Keyword(s):  
Literature Review ◽  
Optimization Problems ◽  
Classical Method ◽  
Optimization Methods ◽  
Function Evaluation ◽  
Practical Applications ◽  
Design Variables ◽  
Non Linear ◽  
Upper Level ◽  
Work Done

Abstract Bi-level optimization is an emerging scope of research which consists of two optimization problems, where the lower-level optimization problem is nested into the upper-level problem as a constraint. Bi-level programming has gained much attention recently for practical applications. Bi-level Programming Problems (BLPP) can be solved with classical and heuristic optimization methods. However, applying heuristic methods, though easier to formulate for realistic complex design, are likely to be too computationally expensive for solving bi-level problems, especially when the problem has high function evaluation cost associated with handling large number of constraint functions. Thus, classical approaches are investigated in this paper. As we present, there appears to be no universally best classical method for solving any kind of NP-hard BLPP problem in terms of accuracy to finding true optimal solutions and minimal computational costs. This could cause a dilemma to the researcher in choosing an appropriate classical approach to solve a BLPP in different domains and levels of complexities. Therefore, this motivates us to provide a detailed literature review and a comparative study of the work done to date on applying different classical approaches in solving constrained non-linear, bi-level optimization problems considering continuous design variables and no discontinuity in functions.

Design of Compressor Impeller Using Evolutionary Optimization Technique

2012 ◽  
Vol 271-272 ◽  
pp. 833-837 ◽  
Author(s):  
Soo Yong Cho ◽  
Jin Han Kim ◽  
Chae Sil Kim
Keyword(s):  
Design Variable ◽  
Pressure Ratio ◽  
Optimization Technique ◽  
Configuration Design ◽  
Neural Net ◽  
Blade Profile ◽  
Design Variables ◽  
Compressor Impeller ◽  
Gradient Based ◽  
The Difference

Configuration design on an impeller using to the centrifugal compressor of turbocharger was conducted to improve its performance. Impeller shape was adjusted by changing its meridional contours and blade profile. Total nine design variables were chosen with constraints. ANN (Artificial Neural Net) was adopted as a main optimization algorithm with PSO (Particle Swarm Optimization) in order to reduce the optimization time. This ANN was learned and trained with the design variable sets which were obtained using DOE (Design of Experiment). This ANN was continuously improved its accuracy for each generation of which population was one hundred. New design variable set in each generation was selected using a non-gradient based method of PSO in order to obtain the global optimized result. After 7th generation, the difference of efficiency and pressure ratio predicted by ANN and CFD (Computational Fluid Dynamics) was less than 0.6%. From more than 1,200 design variable sets, a pareto of efficiency versus pressure ratio was obtained and an optimized result was selected based on the multi-objective function. On this optimized impeller, the efficiency and pressure ratio were improved by 1% and 9.3%, respectively.

Optimum variable input speed for kinematic performance of Geneva mechanisms using teaching-learning-based optimization algorithm

2015 ◽  
Vol 231 (10) ◽  
pp. 1871-1883 ◽  
Author(s):  
WY Lin ◽  
YH Tsai ◽  
KM Hsiao
Keyword(s):  
Optimum Design ◽  
Angular Position ◽  
Angular Acceleration ◽  
Optimization Methods ◽  
Polynomial Coefficients ◽  
Design Variables ◽  
Teaching Learning Based Optimization ◽  
Kinematic Performance ◽  
The Difference ◽  
Teaching Learning

An optimum design of variable input speed for the Geneva mechanism is aimed at improving the kinematic performance of the traditional Geneva mechanism by eliminating infinite angular jerks and reducing the peak angular acceleration of the Geneva wheel during the indexing motion. The normalized angular velocity and acceleration of the Geneva wheel corresponding to the normalized time are introduced. A polynomial function of the normalized time is used to describe the normalized angular position of the crank, and therefore, the corresponding polynomial coefficients are considered as the design variables. The optimum design task is very specialized and difficult to solve with some evolutionary and swarm optimization methods because of the extremely large range for the value of the design variable, arising from the utilization of a higher order polynomial for the normalized time parameter with a value between 0 and 1. A new evolutionary algorithm termed teaching-learning-based optimization comprises a teacher phase and a learner phase. In the teacher phase, the entire population can be gradually shifted to a more promising region, which may be very far from the relatively small initial region. The obtained optimal results are compared with those obtained using the length-adjustable deriving link method discussed in the literature. The findings show that the difference in the effectiveness of the variable input speed method and the length-adjustable driving link method for the reduction of the peak angular acceleration of the Geneva wheel is small.

Active control of a non-linear landing gear system having oleo pneumatic shock absorber using robust linear quadratic regulator approach

2017 ◽  
Vol 232 (13) ◽  
pp. 2397-2411 ◽  
Author(s):  
Hakan Yazici ◽  
Mert Sever
Keyword(s):  
State Space ◽  
Active Control ◽  
Shock Absorber ◽  
Linear Quadratic Regulator ◽  
Landing Gear ◽  
Gear System ◽  
Superior Performance ◽  
Linear Quadratic ◽  
Non Linear ◽  
Pneumatic Shock

This paper deals with the active control of a non-linear active landing gear system equipped with oleo pneumatic shock absorber. Runway induced vibration can cause reduction of pilot’s capability of control the aircraft and results the safety problem before take-off and after landing. Moreover, passenger–crew comfort is adversely affected by vertical vibrations of the fuselage. The active landing gears equipped with oleo pneumatic shock absorber are highly non-linear systems. In this study, uncertain polytopic state space representation is developed by modelling the pneumatic shock absorber dynamics as a mechanical system with non-linear stiffness and damping properties. Then, linear matrix inequalities-based robust linear quadratic regulator controller having pole location constraints is designed, since the classical linear quadratic regulator control design is dealing with linearized state space models without considering the non-linearities and uncertainties. Thereafter, numerical simulation studies are carried out to analyse aircraft response during taxiing. Bump- and random-type runway irregularities are used with various runway class and wide range of longitudinal speed. Simulation results revealed that neglecting the non-linear dynamics associated with oleo pneumatic shock absorber results significant performance degradation. Consequently, it is demonstrated that proposed robust linear quadratic regulator controller has a superior performance in terms of passenger–crew comfort and operational safety when compared to classical linear quadratic regulator.

scholarly journals Application of the topological sensitivity method to the reconstruction of plasma equilibrium domain in a tokamak

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Mohamed Abdelwahed ◽  
Nejmeddine Chorfi ◽  
Maatoug Hassine ◽  
Imen Kallel
Keyword(s):  
Inverse Problem ◽  
Asymptotic Expansion ◽  
Numerical Algorithm ◽  
Shape Function ◽  
Optimization Technique ◽  
Plasma Equilibrium ◽  
Topological Sensitivity ◽  
Sensitivity Method

AbstractThe topological sensitivity method is an optimization technique used in different inverse problem solutions. In this work, we adapt this method to the identification of plasma domain in a Tokamak. An asymptotic expansion of a considered shape function is established and used to solve this inverse problem. Finally, a numerical algorithm is developed and tested in different configurations.

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