Research On Multidisciplinary Robust Design Optimization Considering Parameter And Model Uncertainties

At present,multidisciplinary design optimization(MDO)has achieved a series of research results under parameter uncertainty.However,existing research often fails to consider the model uncertainty.A large number of facts indicate that there are uncertainties in the computer simulation model and metamodel(also called surrogate model)of complex mechanical systems.Due to the multi-parameter,multi-constraint,and strong coupling characteristics of complex mechanical systems,the uncertain MDO research must consider the interaction between parameter and model uncertainties.At present,domestic and foreign MDO studies on comprehensive consideration of parameter and model uncertainties are very rare.Therefore,this paper carried out a multidisciplinary robust design optimization(MRDO)method considering the parameter and model uncertainties.The main work of this article is summarized as follows:(1)A MRDO method considering parameter uncertainty is proposed.This method uses the maximum variation analysis(MVA)method to perform parameter uncertainty analysis,establishes an internal and external nested optimization framework for the system and subsystems,and uses the framework to solve the robust and optimal solutions of the system and subsystems,respectively.The analytical target cascading(ATC)is used to divide and coordinate complex systems to achieve coordination between the system and the subsystems,thereby ensuring the consistency of the robust solutions of the system and subsystems.The proposed method is verified by an MDO mathematical example and a heart dipole optimization problem.This method provides a new attempt to study MRDO problems with parameter uncertainty.The optimization framework combining MVA and ATC methods is simple and easy,which facilitates the efficient optimization of complex systems.(2)A MRDO method with parameter and model uncertainties is proposed.This method uses the interval method to quantify parameter uncertainty.Bayesian method is used to quantify the model uncertainty,i.e.,adding a bias function to the computer model to eliminate the deviation of the computer model from the actual physical system output to the greatest extent.A sufficient number of multidisciplinary feasible samples are obtained through an efficient collaboration model.Then Gaussian process(GP)models of the computer model and the bias function are established using the obtained samples,respectively.A MRDO framework considering parameter and model uncertainties is constructed.The method is verified by an MDO mathematical example and a power converter design problem.This method considers the model uncertainty in multidisciplinary systems,and avoids complex multidisciplinary calculations through the collaboration model,improving computational efficiency.(3)A MRDO method with parameter and metamodeling uncertainties is proposed.The effects of the combined effects of parameter and metamodel uncertainties on system performance are discussed.The collaboration model is used to obtain samples that meet the requirements of multidisciplinary characteristics.GP metamodels of the computational model are constructed by obtained samples.Then GP metamodels are evaluated to ensure the required accuracy.Monte Carlo simulation(MCS)method is used to quantify the compound effect of parameter and the metamodeling uncertainties.A MRDO optimization framework considering parameter and metamodeling uncertainties is established.An MDO mathematical example and reducer design problem verify the method.This method explores the metamodeling uncertainty in a multidisciplinary system and improves the robustness of the optimization results.(4)A MRDO method with parameter,model,and metamodeling uncertainties is proposed.This method uses interval method to quantify parameter uncertainty.Bayesian method is taken to quantify model uncertainty.The MCS method is used to quantify the combined effects of parameter,model,and metamodeling uncertainties in MRDO.An MRDO platform is established under the parameter,model,and metamodeling uncertainties.The method is verified by an MDO mathematical example and thin-walled pressure vessel design example.This method comprehensively considers the parameters,model,and metamodel uncertainties in complex systems,and provides useful explorations and attempts for robust optimization of complex systems in engineering practice.(5)Taking the design optimization problem of electric vehicle liquid-cooled battery thermal management system(BTMS)as the background,the MRDO study of square BTMS and cylindrical BTMS is carried out by using the proposed method in this paper.The parameter,model,and metamodel uncertainties in BTMS optimization are discussed.The successful application of the proposed MRDO approach in the engineering example provides some reference for the design optimization of BTMS.