Multi-objective optimization of vehicle seat suspension with friction under random excitation

2021 ◽  
pp. 095440702110555
Author(s):  
Rongkang Luo ◽  
Peibao Wu ◽  
Jiabin Luo ◽  
Zhichao Hou ◽  
Le He ◽  
...  
Keyword(s):  
Linear Model ◽  
Human Body ◽  
Random Excitation ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Seat Suspension ◽  
Model Linearization ◽  
Vehicle Seat

A seat suspension contributes greatly to vehicle ride comfort as a result of direct contact with the human body. Friction in a seat suspension produces strong non-smooth nonlinearity in seat dynamics, which makes the simulation-based optimization on the seat suspension’s performance time-consuming. This study tries to address parameter optimization on a vehicle seat suspension with the friction force in an analytical approach. A two degrees of freedom model is firstly established for the human body-seat system with friction and subjected to bandlimited random excitation. The nonlinear model is converted into an equivalent linear model by using Gaussian linearization. The dynamic responses of the linear model have then derived analytically and validated by Monte Carlo simulations. Based on the analytical solution, a multi-objective optimization strategy is proposed for the seat suspension. The acceleration of the human body and the suspension travel are chosen as the objective indexes to evaluate seat performance. Simulation results show that the proposed optimization strategy is efficient, where a global optimum is guaranteed owing to the analytical expression of the objective function. The optimization approach taking advantage of model linearization can be applied to similar mechanical systems with friction.

Modern Compressor Aerodynamic Blading Process Using Multi-Objective Optimization

2006 ◽  
Author(s):  
Akin Keskin ◽  
Amit Kumar Dutta ◽  
Dieter Bestle
Keyword(s):  
High Efficiency ◽  
Axial Compressor ◽  
Design Flow ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Flow Conditions ◽  
Multi Objective ◽  
Blade Section ◽  
The Individual

Aerodynamic design of an axial compressor is a challenging design task requiring a compromise between contradicting requirements like wide operating range, high efficiency, low number of stages and high surge margin. Therefore, the design process is typically subdivided into a sequence of subproblems where the blading design is a key process. According to flow conditions, which result from throughflow calculations on axis-symmetric stream surfaces, 2-dimensional blade profiles have to be designed, which then may be stacked along a radial stacking line in order to find the 3D-blade geometry. The design of the blade sections is rather time consuming due to many iterations with different programs. Usually a geometry generation tool is used to describe the blade sections which are then evaluated by a blade-to-blade CFD solver. The quality of a single blade section is typically characterized by the overall loss at design flow conditions and the working range determined by an amount of loss increase due to incidence variation. The aerodynamic performance of the final airfoils and thus of the whole compressor depends significantly on the design of the individual blade sections. In this investigation an automated multi-objective optimization strategy is developed to find best blade section geometries with respect to loss and working range. The multi-objective optimization approach provides Pareto-optimal compromise solutions at reasonable computational costs outperforming a given Rolls-Royce datum design which has been ‘optimized’ manually by a human design engineer.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

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