Design-space adaptation method for multiobjective and multidisciplinary optimization

This paper developed a new method that adaptively adjusts a design space by considering the actual solution distribution of a problem to overcome the conventional design-space adaptation method that assumes the solutions distribution to be a normal distribution because the distributions of solutions are rarely normal distributions for real-world problems.The developed method was applied to nineteen multiobjective test functions that are widely used to evaluate the characteristics and performance of optimization approaches.The results showed that this method adapted the design space to an appropriate design space where the solution existence probability was high.The optimization performance achieved using the developed method was higher than that of the conventional methods.Furthermore,the developed method was applied to the conceptual design of an unmanned spacecraft to confirm its validity in real-world design and multidisciplinaryoptimization problems.The results showed that the Pareto solutions of the developed method were superior to those of conventional methods.Additionally,the optimization efficiency with the developed method was improved by more than 1.4 times over that of the conventional methods.In this regard,the developed method has the potential to be applied to complicated real-world optimization problems to achieve better performance and efficiency.

Multidisciplinary design optimization of a dual-spin guided vehicle

In this research,a Multidisciplinary Design Optimization approach is proposed for the dual-spin guided flying projectile design considering external and internal parts of the body as design variables.In this way,a parametric formulation is developed.All related disciplines,including structure,aerodynamics,guidance,and control are considered.Minimum total mass,maximum aerodynamic control effectiveness,minimum miss distance,maximum yield stress in all subsystems,controllability and gyroscopic stability constraints are some of objectives/constraints taken into account.The problem is formulated in All-At-Ones Multidisciplinary Design Optimization approach structure and solved by Simulated Annealing and minimax algorithms.The optimal configurations are evaluated in various aspects.The resulted optimal configurations have met all design objectives and constraints.

Multidisciplinary Design Optimization of UAV Considering Development Costs

The paper establishes a multidisciplinary design and optimization framework that aims at minimizing Unmanned aerial vehicles (UAV) development costs. This framework integrates development costs as an equally important factor alongside weight and aerodynamic disciplines within the UAV design process. The OpenMDAO paradigm is employed to facilitate a standalone design and optimization application. A comprehensive multidisciplinary analysis module is developed, encompassing initial geometrical sizing, weight analysis, aerodynamic performance evaluation, and estimation models for development costs. The effectiveness of the framework is validated through a low-cost, high-performance UAV case study. The results demonstrate that neglecting the influence of UAV development costs would be imprudent. By appropriately adjusting design parameters using the optimization algorithm, significant reductions in UAV development costs can be achieved with minimal performance losses.

An Efficient Method for Reliability-based Multidisciplinary Design Optimization

Design for modem engineering system is becoming multidisciplinary and incorporates practical uncertainties; therefore, it is necessary to synthesize reliability analysis and the multidisciplinary design optimization （MDO） techniques for the design of complex engineering system. An advanced first order second moment method-based concurrent subspace optimization approach is proposed based on the comparison and analysis of the existing multidisciplinary optimization techniques and the reliability analysis methods. It is seen through a canard configuration optimization for a three-surface transport that the proposed method is computationally efficient and practical with the least modification to the current deterministic optimization process.

MULTIDISCIPLINARY ROBUST OPTIMIZATION DESIGN

Because uncertainty factors inevitably exist under multidisciplinary designenvironment, a hierarchical multidisciplinary robust optimization design based on response surfaceis proposed. The method constructs optimization model of subsystem level and system level tocoordinate the coupling among subsystems, and also the response surface based on the artificialneural network is introduced to provide information for system level optimization tool to maintainthe independence of subsystems, i.e. to realize multidisciplinary parallel design. The applicationcase of electrical packaging demonstrates that reasonable robust optimum solution can be yielded andit is a potential and efficient multi-disciplinary robust optimization approach.

Multidisciplinary design optimization for air-condition production system based on multi-agent technique

In order to guarantee the overall production performance of the multiple departments in an air-condition production industry, multidisciplinary design optimization model for production system is established based on the multi-agent technology. Local operation models for departments of plan, marketing, sales, purchasing, as well as production and warehouse are formulated into individual agents, and their respective local objectives are collectively formulated into a multi-objective optimization problem. Considering the coupling effects among the correlated agents, the optimization process is carried out based on self-adaptive chaos immune optimization algorithm with mutative scale. The numerical results indicate that the proposed multi-agent optimization model truly reflects the actual situations of the air-condition production system. The proposed multi-agent based multidisciplinary design optimization method can help companies enhance their income ratio and profit by about 33% and 36%, respectively, and reduce the total cost by about 1.8%.

Multidisciplinary Design Optimization of Crash Box with Negative Poisson’s Ratio Structure

To improve the crashworthiness and energy absorption performance,a novel crash box negative Poisson’s ratio(NPR)structure is proposed according to the characteristics of low speed collision of bumper system.Taking the peak collision force and the average collision force as two subsystems,a multidisciplinary collaborative optimization design is carried out,and its optimization results are compared with the ones optimized by NSGA-II algorithm.Simulation results show that the crashworthiness and energy absorption performance of the novel crash box is improved effectively based on the multidisciplinary optimization method.

EFFICIENT METHOD FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION BY CONSIDERING UNCERTAINTY

A new reliability-based multidisciplinary design optimization （RBMDO） framework is proposed by combining the single-loop-based reliability analysis （SLBRA） method with multidisciplinary feasible （MDF） method. The Kriging approximate model with updating is introduced to reduce the computational cost of MDF caused by the complex structure. The computational efficiency is remarkably improved as the lack of iterative process during reliability analysis. Special attention is paid to a turbine blade design optimization by adopting the proposed method. Results show that the method is much more efficient than the commonly used double-loop based RBMDO method. It is feasible and efficient to apply the method to the engineering design.

Research on disruptive design pre-identification in the preliminary design phase of aero engine flow pass multidisciplinary optimization

In aero engine design, determining whether the preliminary design will have disruptive effects on the detailed design is the key to multidisciplinary design optimization in the preliminary design stage. In order to adapt to the non-orthogonal parameter value range caused by the selfconstrained parametric modeling method, a non-orthogonal space mapping method that maps the optimal Latin hypercube sampling points of the traditional orthogonal design space to the non-orthogonal design space is proposed. Based on the logical regression method in machine learning field, a kind of feasible domain boundary identification method is employed to identify whether the sample spatial response meets the relevant criteria. The method proposed in this paper is used to identify and analyze the key technologies of the high-pressure turbine mortise joint structure. It is found that the preliminary design of the aero engine may lead to the failure to obtain a mortise joint structure meeting the design requirements in the detailed design stage. The mortise joint structure needs to be pre-optimized in the preliminary design stage.

A Multidisciplinary Optimization Architecture for A Distributed Computing Environment

This paper introduces a distributed computing approach for multidisciplinary complex product design. The framework that may aid in the implementation of this scheme is described. This distributed design environment has the property of concurrent engineering. The approaches considered here are the framework of a distributed optimization design environment and the optimization architecture in the environment.

Multidisciplinary Design Optimization of Vehicle Instrument Panel Based on Multi-objective Genetic Algorithm

Typical multidisciplinary design optimization（MDO） has gradually been proposed to balance performances of lightweight, noise, vibration and harshness（NVH） and safety for instrument panel（IP） structure in the automotive development. Nevertheless, plastic constitutive relation of Polypropylene（PP） under different strain rates, has not been taken into consideration in current reliability-based and collaborative IP MDO design. In this paper, based on tensile test under different strain rates, the constitutive relation of Polypropylene material is studied. Impact simulation tests for head and knee bolster are carried out to meet the regulation of FMVSS 201 and FMVSS 208, respectively. NVH analysis is performed to obtain mainly the natural frequencies and corresponding mode shapes, while the crashworthiness analysis is employed to examine the crash behavior of IP structure. With the consideration of lightweight, NVH, head and knee bolster impact performance, design of experiment（DOE）, response surface model（RSM）, and collaborative optimization（CO） are applied to realize the determined and reliability-based optimizations, respectively. Furthermore, based on multi-objective genetic algorithm（MOGA）, the optimal Pareto sets are completed to solve the multi-objective optimization（MOO） problem. The proposed research ensures the smoothness of Pareto set, enhances the ability of engineers to make a comprehensive decision about multi-objectives and choose the optimal design, and improves the quality and efficiency of MDO.

Reliability-based multidisciplinary design optimization using incremental shifting vector strategy and its application in electronic product design

Use of multidisciplinary analysis in reliabilitybased design optimization(RBDO) results in the emergence of the important method of reliability-based multidisciplinary design optimization(RBMDO). To enhance the efficiency and convergence of the overall solution process,a decoupling algorithm for RBMDO is proposed herein.Firstly, to decouple the multidisciplinary analysis using the individual disciplinary feasible(IDF) approach, the RBMDO is converted into a conventional form of RBDO. Secondly,the incremental shifting vector(ISV) strategy is adopted to decouple the nested optimization of RBDO into a sequential iteration process composed of design optimization and reliability analysis, thereby improving the efficiency significantly. Finally, the proposed RBMDO method is applied to the design of two actual electronic products: an aerial camera and a car pad. For these two applications, two RBMDO models are created, each containing several finite element models(FEMs) and relatively strong coupling between the involved disciplines. The computational results demonstrate the effectiveness of the proposed method.

Effect of Variable Selection on Multidisciplinary Design Optimization:a Flight Vehicle Example

Different multidisciplinary design optimization （MDO） problems are formulated and compared. Two MDO formulations are applied to a sounding rocket in order to optimize the performance of the rocket. In the MDO of the referred vehicle, three disciplines have been considered, which are trajectory, propulsion and aerodynamics. A special design structure matrix is developed to assist data exchange between disciplines. This design process uses response surface method （RSM） for multidisciplinary optimization of the rocket. The RSM is applied to the design in two categories： the propulsion model and the system level. In the propulsion model, RSM determines an approximate mathematical model of the engine output parameters as a function of design variables. In the system level, RSM fits a surface of objective function versus design variables. In the first MDO problem formulation, two design variables are selected to form propulsion discipline. In the second one, three new design variables from geometry are added and finally, an optimization method is applied to the response surface in the system level in order to find the best result. Application of the first developed multidisciplinary design optimization procedure increased accessible altitude （performance index） of the referred sounding rocket by twenty five percents and the second one twenty nine.

Multidisciplinary design optimization on production scale of underground metal mine

In order to ensure overall optimization of the underground metal mine production scale, multidisciplinary design optimization model of production scale which covers the subsystem objective function of income of production, safety and environmental impact in the underground metal mine was established by using multidisciplinary design optimization method. The coupling effects from various disciplines were fully considered, and adaptive mutative scale chaos immunization optimization algorithm was adopted to solve multidisciplinary design optimization model of underground metal mine production scale. Practical results show that multidisciplinary design optimization on production scale of an underground lead and zinc mine reflect the actual operating conditions more realistically, the production scale is about 1.25 Mt/a (Lead and zinc metal content of 160 000 t/a), the economic life is approximately 14 a, corresponding coefficient of production profits can be increased to 15.13%, safety factor can be increased to 5.4% and environmental impact coefficient can be reduced by 9.52%.

Multidisciplinary Design Optimization of A Human Occupied Vehicle Based on Bi-Level Integrated System Collaborative Optimization

The design of Human Occupied Vehicle （HOV） is a typical multidisciplinary problem, but heavily dependent on the experience of naval architects at present engineering design. In order to relieve the experience dependence and improve the design, a new Multidisciplinary Design Optimization （MDO） method ＂Bi-Level Integrated System Collaborative Optimization （BLISCO）＂ is applied to the conceptual design of an HOV, which consists of hull module, resistance module, energy module, structure module, weight module, and the stability module. This design problem is defined by 21 design variables and 23 constraints, and its objective is to maximize the ratio of payload to weight. The results show that the general performance of the HOV can be greatly improved by BLISCO.

Uncertain Multidisciplinary Design Optimization on Next Generation Subsea Production System by Using Surrogate Model and Interval Method

The innovative Next Generation Subsea Production System(NextGen SPS)concept is a newly proposed petroleum development solution in ultra-deep water areas.The definition of NextGen SPS involves several disciplines,which makes the design process difficult.In this paper,the definition of NextGen SPS is modeled as an uncertain multidisciplinary design optimization(MDO)problem.The deterministic optimization model is formulated,and three concerning disciplines—cost calculation,hydrodynamic analysis and global performance analysis are presented.Surrogate model technique is applied in the latter two disciplines.Collaborative optimization(CO)architecture is utilized to organize the concerning disciplines.A deterministic CO framework with two disciplinelevel optimizations is proposed firstly.Then the uncertainties of design parameters and surrogate models are incorporated by using interval method,and uncertain CO frameworks with triple loop and double loop optimization structure are established respectively.The optimization results illustrate that,although the deterministic MDO result achieves higher reduction in objective function than the uncertain MDO result,the latter is more reliable than the former.

APPLICATION OF HYBRID GENETIC ALGORITHM IN AEROELASTIC MULTIDISCIPLINARY DESIGN OPTIMIZATION OF LARGE AIRCRAFT

The genetic/gradient-based hybrid algorithm is introduced and used in the design studies of aeroelastic optimization of large aircraft wings to attain skin distribution,stiffness distribution and design sensitivity.The program of genetic algorithm is developed by the authors while the gradient-based algorithm borrows from the modified method for feasible direction in MSC/NASTRAN software.In the hybrid algorithm,the genetic algorithm is used to perform global search to avoid to fall into local optima,and then the excellent individuals of every generation optimized by the genetic algorithm are further fine-tuned by the modified method for feasible direction to attain the local optima and hence to get global optima.Moreover,the application effects of hybrid genetic algorithm in aeroelastic multidisciplinary design optimization of large aircraft wing are discussed,which satisfy multiple constraints of strength,displacement,aileron efficiency,and flutter speed.The application results show that the genetic/gradient-based hybrid algorithm is available for aeroelastic optimization of large aircraft wings in initial design phase as well as detailed design phase,and the optimization results are very consistent.Therefore,the design modifications can be decreased using the genetic/gradient-based hybrid algorithm.

Applying the disciplinary relation matrix to multidisciplinary design optimization modeling and solving

A new efficient coupling relationship description method has been developed to provide an automated and visualized way to multidisciplinary design optimization (MDO) modeling and solving. The disciplinary relation matrix (DRM) is proposed to describe the coupling relationship according to disciplinary input/output variables, and the MDO definition has been reformulated to adopt the new interfaces. Based on these, a universal MDO solving procedure is proposed to establish an automated and efficient way for MDO modeling and solving. Through a simple and convenient initial configuration, MDO problems can be solved using any of available MDO architectures with no further effort. Several examples are used to verify the proposed MDO modeling and solving process. Result shows that the DRM method has the ability to simplify and automate the MDO procedure, and the related MDO framework can evaluate the MDO problem automatically and efficiently.

Multidisciplinary Design Optimization with a New Effective Method

Collaborative optimization （CO） is one of the most widely used methods in multidisciplinary design optimization （MDO）, which is an effective methodology to solve modem complex engineering problems. CO consists of two-level optimization problems which are system optimization problem and subspace optimization problem. The architecture of CO can reserve the autonomy of individual disciplines in maximum, while providing a mechanism for coordinating design problem. However, CO has low computation efficiency and is easy to diverge. For the purpose of solving these problems, the former improved methods were studied. The relaxation factors were used to change the system consistency constraints to inequality constraints, or the response surface estimation was used to surrogate the system consistency constraints. However, these methods didn＇t avoid the computational difficulties very well, furthermore, some new problems arose. The concept of optimum constraint sensitivity was proposed, and the quadratic constraints in system level were reformed. Hence, a new collaborative optimization was developed, which is called system level dynamic constraint collaborative optimization （DCCO）. The novel method is able to increase the exchange of information between system level and disciplinary level. The optimization results of each disciplinary optimization can be feedback to system level with the optimum constraint sensitivity. On the basis of the information, the new system level linear dynamic constraints can be constructed; it is better to reflect the effect of disciplinary level optimizations. The system level optimizer can clearly capture the boundary where disciplinary objective functions become zero, and considerably enhance the convergence. Two standard MDO examples were conducted to verify the feasibility and effectiveness of DCCO. The results show that DCCO can save the solving time, and is much better in terms of convergence and robustness, hence, the new method is more efficient.

Multidisciplinary Modeling and Optimization Method of Remote Sensing Satellite Parameters Based on SysML-CEA

To enhance the efficiency of system modeling and optimization in the conceptual design stage of satellite parameters,a system modeling and optimization method based on System Modeling Language and Co-evolutionary Algorithm is proposed.At first,the objectives of satellite mission and optimization problems are clarified,and a design matrix of discipline structure is constructed to process the coupling relationship of design variables and constraints of the orbit,payload,power and quality disciplines.In order to solve the problem of increasing nonlinearity and coupling between these disciplines while using a standard collaborative optimization algorithm,an improved genetic algorithm is proposed and applied to system-level and discipline-level models.Finally,the CO model of satellite parameters is solved through the collaborative simulation of Cameo Systems Modeler(CSM)and MATLAB.The result obtained shows that the method proposed in this paper for the conceptual design phase of satellite parameters is efficient and feasible.It can shorten the project cycle effectively and additionally provide a reference for the optimal design of other complex projects.