Self-adjusting multidisciplinary design of hydraulic engine mount using bond graphs and inductive genetic programming

Tabatabaei, S. Karim; Behbahani, Saeed; Silva, Clarence W. de

doi:10.1016/j.engappai.2015.10.010

Cited by 10 publications

(9 citation statements)

References 20 publications

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“…In the research of bond graph simulation software systems, researchers have also developed some related modeling and simulation software. With the help of these developed software such as ENPORT [24], BGGP [25], 20sim [36], etc., computer visualization of bond graph models can be realized. And the system can be simulated and analyzed.…”

Section: Bond Graphmentioning

confidence: 99%

Quantitative Selection Method of Product Conceptual Scheme Based On Bond Graph

Jiang

et al. 2020

Preprint

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The optimum conceptual scheme is mostly selected based on qualitative decision-making evaluation methods in conceptual design stage of engineering product, which lacks quantitative verification and evaluation. To solve this problem, quantitative selection method of product conceptual scheme based on bond graph is proposed. Through analyzing the correlation between function, effect and bond element, the bond graph model and corresponding AMESim simulation model of conceptual scheme are constructed to verify the feasibility of conceptual scheme, and the quantitative evaluation index of function-performance requirements is calculated. A conceptual scheme selection method considering quantitative evaluation index and qualitative evaluation by experts is proposed to obtain the optimum scheme. The conceptual scheme selection of transmission system of a shearer cutting unit is taken as an example to verify the effectiveness of the proposed method.

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Section: Bond Graphmentioning

confidence: 99%

Quantitative Selection Method of Product Conceptual Scheme Based On Bond Graph

Jiang

et al. 2020

Preprint

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“…To validate the accuracy of generated metamodels, a validation data set of 100 sampling points was also used to check the overall performance capacity of the metamodels. In this paper, the relative error was used to measure the discrepancy between the actual response and the predicted model, e = r a − r p /r a (13) where r a is the actual response value and r p is the prediction value. The relative errors of 100 validation points are given in Figure 7 and their relative mean errors for all six responses were 0.083%, 0.008%, 0.024%, 0.119%, 0.106%, and 0.598%.…”

Section: Case Studymentioning

confidence: 99%

“…into consideration simultaneously and has been employed in large modern aircraft designs [4][5][6][7][8][9]. In light of its capability to overcome the limitations of the conventional method, the MAO technique with the integration of novel algorithms has been applied to solve complex problems in other industry sectors, such as automobile [10,11], energy [12], mechatronic systems [13], etc. Obviously, a crucial challenge of MAO is its massive computation burden, especially for computational fluid dynamics (CFD) simulations based on Reynolds-averaged Naiver-Stokes (RANS) equations.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Approaches to Solve a Complex Aircraft Performance Optimization Problem

2019

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The increasing demands for travelling comfort and reduction of carbon dioxide emissions have been considered substantially in the stage of conceptual aircraft design. However, the design of a modern aircraft is a multidisciplinary process, which requires the coordination of information from several specific disciplines, such as structures, aerodynamics, control, etc. To address this problem with adequate accuracy, the multidisciplinary analysis and optimization (MAO) method is usually applied as a systematic and robust approach to solve such complex design issues arising from industries. Since MAO method is tedious and computationally expensive, genetic programming (GP)-based metamodeling techniques incorporating MAO are proposed as an effective approach to minimize the wing stiffness of a large aircraft subject to aerodynamic, aeroelastic and stability constraints in the conceptual design phase. Based on the linear small-disturbance theory, the state-space equation is employed for stability analysis. In the process of multidisciplinary analysis, aeroelastic response simulations are performed using Nastran. To construct metamodels representing the responses of the interests with high accuracy as well as less computational burden, optimal Latin hypercube design of experiments (DoE) is applied to determine the optimized distribution of sampling points. Following that, parametric optimization is carried out on metamodels to obtain the optimal wing geometry shape, elastic axis positions and stiffness distribution, and then the solution is verified by finite element simulations. Finally, the superiority of the GP-based metamodel technique over genetic algorithm is demonstrated by multidisciplinary design optimization of a representative beam-frame wing structure in terms of accuracy and efficiency. The results also show that GP metamodel-based strategy for solving MAO problems can provide valuable insights to tailoring parameters for the effective design of a large aircraft in the conceptual phase.

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“…For control purposes, the model given by Equations (16)(17)(18)(19) is converted into a global dynamic model by using the transformation matrix presented in Equation 6. The dynamic model of the robot in the global frame can be written as follows:…”

Section: Mathematical Model Extraction From the Integrated Bond Graphmentioning

confidence: 99%

Intuitive dynamic modeling and flatness-based nonlinear control of a mobile robot

2017

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In this paper, a bond graph model of a mobile robot with four Mecanum wheels is developed to extract a dynamic model of the robot. This is achieved using the BG_V21 tool box of MATLAB. The dynamic model thus obtained is used to derive the control law for trajectory tracking by the robot. There are two control algorithms that are used, namely, the flatness-based controller and the backstepping controller. From the simulation results, it is evident that the extracted dynamic model of the robot is accurate. Moreover, the flatness-based controller proved to have the upper hand in performance over the backstepping controller.

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Self-adjusting multidisciplinary design of hydraulic engine mount using bond graphs and inductive genetic programming

Cited by 10 publications

References 20 publications

Quantitative Selection Method of Product Conceptual Scheme Based On Bond Graph

Quantitative Selection Method of Product Conceptual Scheme Based On Bond Graph

Metaheuristic Approaches to Solve a Complex Aircraft Performance Optimization Problem

Intuitive dynamic modeling and flatness-based nonlinear control of a mobile robot

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