DESIGN OPTIMIZATION OF VEHICLE SUSPENSIONS WITH A QUARTER-VEHICLE MODEL

2008 ◽  
Vol 32 (2) ◽  
pp. 297-312 ◽  
Author(s):  
Zhongzhe Chi ◽  
Yuping He ◽  
Greg F. Naterer
Keyword(s):  
Comparative Study ◽  
Design Optimization ◽  
Search Algorithm ◽  
Pattern Search ◽  
Vehicle Body ◽  
Design Criteria ◽  
Vehicle Speed ◽  
Vehicle Model ◽  
Vehicle Suspensions ◽  
Design Variables

This paper presents a comparative study of three optimization algorithms, namely Genetic Algorithms (GAs), Pattern Search Algorithm (PSA) and Sequential Quadratic Program (SQP), for the design optimization of vehicle suspensions based on a quarter-vehicle model. In the optimization, the three design criteria are vertical vehicle body acceleration, suspension working space, and dynamic tire load. To implement the design optimization, five parameters (sprung mass, un-sprung mass, suspension spring stiffness, suspension damping coefficient and tire stiffness) are selected as the design variables. The comparative study shows that the global search algorithm (GA) and the direct search algorithm (PSA) are more reliable than the gradient based local search algorithm (SQP). The numerical simulation results indicate that the design criteria are significantly improved through optimizing the selected design variables. The effect of vehicle speed and road irregularity on design variables for improving vehicle ride quality has been investigated. A potential design optimization approach to the vehicle speed and road irregularity dependent suspension design problem is recommended.

Multi Objective Design Optimization of Helical Gear Pair Using Adaptive Parameter Harmony Search Algorithm

2015 ◽  
Vol 813-814 ◽  
pp. 1032-1036
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Jeganathan ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Search Algorithm ◽  
Harmony Search ◽  
Helical Gear ◽  
Harmony Search Algorithm ◽  
Gear Pair ◽  
Multi Objective ◽  
Gear Design ◽  
Design Variables ◽  
Helical Gear Pair

Mechanical design engineers design products by selecting the best possible materials and geometries that satisfies the specific operational requirements of the design. It involves lot of creativity and aesthetics to make better designs. A gear design makes the designer to compromise many design variables so as to arrive the best performance of a gear set. The best possible way for multi variable, Multiobjective gear design is to try design optimization. For many complex engineering optimization problems multi objective design optimization methods are used to simplify the design problem. In this paper, multiobjective design of helical gear pair transmission with objective functions namely volume of the small and large helical gear and opposite number of overlap ratio is taken into account. The design variables considered are normal module, helix angle, gear width coefficient and teeth number of small helical gear. A recent meta-heuristic algorithm namely parameter adaptive harmony search algorithm is applied to solve this problem using the weighted sum approach. It is evident from the results that the proposed approach is performing better than other algorithms.

scholarly journals Brass Instruments Design Using Physics-Based Sound Simulation Models and Surrogate-Assisted Derivative-Free Optimization

2016 ◽  
Author(s):  
Robin Tournemenne ◽  
Jean-François Petiot ◽  
Bastien Talgorn ◽  
Michael Kokkolaras
Keyword(s):  
Design Optimization ◽  
Search Algorithm ◽  
Rank Order ◽  
Computational Cost ◽  
Simulation Models ◽  
Function Evaluation ◽  
Brass Instruments ◽  
Design Variables ◽  
Direct Search Algorithm ◽  
Sound Simulation

This paper presents a method for design optimization of brass wind instruments. The shape of a trumpet’s bore is optimized to improve intonation using a physics-based sound simulation model. This physics-based model consists of an acoustic model of the resonator (input impedance), a mechanical model of the excitator (the lips of a virtual musician) and a model of the coupling between the excitator and the resonator. The harmonic balance technique allows the computation of sounds in a permanent regime, representative of the shape of the resonator according to control parameters of the virtual musician. An optimization problem is formulated, in which the objective function to be minimized is the overall quality of the intonation of the different notes played by the instrument (deviation from the equal-tempered scale). The design variables are the physical dimensions of the resonator. Given the computationally expensive function evaluation and the unavailability of gradients, a surrogate-assisted optimization framework is implemented using the mesh adaptive direct search algorithm (MADS). Surrogate models are used both to obtain promising candidates in the search step of MADS and to rank-order additional candidates generated by the poll step of MADS. The physics-based model is then used to determine the next design iterate. Two examples (with two and five design optimization variables, respectively) are presented to demonstrate the approach. Results show that significant improvement of intonation can be achieved at reasonable computational cost. The implementation of this method for computer-aided instrument design is discussed, considering different objective functions or constraints based on intonation but also on the timbre of the instrument.

scholarly journals Dynamic Characteristics Analysis of ISD Suspension System under Different Working Conditions

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1345
Author(s):  
Xiaopeng Li ◽  
Fanjie Li ◽  
Dongyang Shang
Keyword(s):  
Ride Comfort ◽  
Vibration Reduction ◽  
Suspension System ◽  
Dynamic Responses ◽  
Vehicle Body ◽  
Vehicle Speed ◽  
Vehicle Model ◽  
System A ◽  
Characteristics Analysis ◽  
Road Excitation

The “inerter-spring-damper” (ISD) suspension system is a suspension system composed of an inerter, spring, and damper. To study the ride comfort and stability of the vehicle by using the ISD suspension system, a vehicle model with ISD suspension is established in this paper. The vehicle model including vertical, pitch, roll, and yaw motion of the vehicle body. Based on the vehicle model, the differential equation of motion with ISD suspension is obtained. The dynamic responses of the ISD suspension system are investigated by using different road excitations. At the same time, the influence of coupled excitation and single excitation on the vibration reduction performance of the ISD suspension system is studied. Then, the dynamic responses of ISD suspension and passive suspension are compared, and the improvement of comprehensive vibration reduction performance of ISD suspension system is quantitatively analyzed. The numerical results illustrate the ISD suspension has the optimal vehicle speed under different road excitations, and the comprehensive vibration reduction performance of the ISD suspension is the best when driving at the optimal vehicle speed. Under different types of road excitation, ISD suspension shows excellent comprehensive vibration reduction performance. ISD suspension is more suitable for vibration reduction of complex roads than that of a single road.

scholarly journals Brass Instruments Design Using Physics-Based Sound Simulation Models and Surrogate-Assisted Derivative-Free Optimization

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Robin Tournemenne ◽  
Jean-François Petiot ◽  
Bastien Talgorn ◽  
Michael Kokkolaras ◽  
Joël Gilbert
Keyword(s):  
Design Optimization ◽  
Search Algorithm ◽  
Rank Order ◽  
Computational Cost ◽  
Simulation Models ◽  
Problem Formulation ◽  
Function Evaluation ◽  
Design Variables ◽  
Direct Search Algorithm ◽  
Sound Simulation

This paper presents a method for design optimization of brass wind instruments. The shape of a trumpet's bore is optimized to improve intonation using a physics-based sound simulation model. This physics-based model consists of an acoustic model of the resonator, a mechanical model of the excitator, and a model of the coupling between the excitator and the resonator. The harmonic balance technique allows the computation of sounds in a permanent regime, representative of the shape of the resonator according to control parameters of the virtual musician. An optimization problem is formulated in which the objective function to be minimized is the overall quality of the intonation of the different notes played by the instrument. The design variables are the physical dimensions of the resonator. Given the computationally expensive function evaluation and the unavailability of gradients, a surrogate-assisted optimization framework is implemented using the mesh adaptive direct search algorithm (MADS). Surrogate models are used both to obtain promising candidates in the search step of MADS and to rank-order additional candidates generated by the poll step of MADS. The physics-based model is then used to determine the next design iterate. Two examples (with two and five design optimization variables) demonstrate the approach. Results show that significant improvement of intonation can be achieved at reasonable computational cost. Finally, the perspectives of this approach for computer-aided instrument design are evoked, considering optimization algorithm improvements and problem formulation modifications using for instance different design variables, multiple objectives and constraints or objective functions based on the instrument's timbre.

Support Optimization for Piping System With Machine Learning

2017 ◽  
Author(s):  
Jongho Ham ◽  
Jungeun An ◽  
Bongjae Kim ◽  
Jaewoong Choi ◽  
Booki Kim
Keyword(s):  
Machine Learning ◽  
Stress Analysis ◽  
Hybrid Method ◽  
Learning Algorithm ◽  
Computation Time ◽  
Pattern Search ◽  
Design Criteria ◽  
Piping System ◽  
Search Optimization ◽  
Design Variables

Piping stress analysis is performed by the manipulations of support type, location and pipe arrangement based on many specific design criteria. A classical way to find good engineering solutions satisfying design criteria among lots of combinations is obviously time-consuming work. In field practice, it also highly depends on engineer’s experiences and abilities. This paper proposes a hybrid method by combining several global search optimization algorithms and predication model generation in order to automatically control the combinations of support types as the engineering solutions. Here, we use some efficient and popular algorithms such as genetic algorithm, swarm intelligence and Gaussian pattern search to develop initial design of experiments. From the set of the initials, we build and update a prediction model by applying machine learning algorithm such as artificial neural network. As a result of using the hybrid method, the engineering solution is sufficiently optimized for the classical solution. Design variables for this problem are the types of restraints (or the pipe support type). The nonlinearity conditions such as gaps and frictions are also treated as key design variables. Each restraint is initially identified as a binary set of design variables, and transformed to integer numbers to run on the n-dimensional design space. The number of dimension corresponds to the number of pipe supports. Currently, pipe stress analysis problems are divided into a certain size that is enough to run on one computer for project management purpose. If we have bigger system with more design variables to consider, the hybrid machine learning method plays a key role in saving computation time with the help of additional parallel computation technique.

Design of a single-DOF kinematic chain using hybrid GA-pattern search and sequential GA

2011 ◽  
Vol 226 (6) ◽  
pp. 1633-1643 ◽  
Author(s):  
A Maheri ◽  
A T Isikveren
Keyword(s):  
Weighting Function ◽  
Search Algorithm ◽  
Pressure Coefficient ◽  
Kinematic Chain ◽  
Search Method ◽  
Pattern Search ◽  
Initial Population ◽  
Design Variables ◽  
Geometric Deviation ◽  
Feasible Solutions

With the aim of obtaining an optimal design for a single-DoF kinematic chain with the function of morphing aerofoils, two search methods are developed. The assessment criteria arethe length of the linkage and the accuracy of mapping. While the former is taken as the objective of optimization, the latter is treated as a constraint. The accuracy in mapping is measured by the aerodynamic performance deviation, a parameter defined by combining the geometric deviation and a weighting function based on the distribution of pressure coefficient. The first search method is based on a genetic algorithm (GA) with an embedded pattern search algorithm. The aim of the pattern search is to reduce the number of failed attempts in generating feasible solutions for the initial population of the GA as well as repairing infeasible individuals produced by reproduction operators. In the second search method, the set of design variables is determined in two consecutive steps by employing a sequential GA. Results of five runs of each search method reveal that while the best solution was produced by the sequential GA search method, hybrid GA-pattern search yielded more consistent results with shorter lengths and better mapping precision in average.

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