scholarly journals A multidisciplinary coupling relationship coordination algorithm using the hierarchical control methods of complex systems and its application in multidisciplinary design optimization

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668522 ◽  
Author(s):  
Rong Yuan ◽  
Haiqing Li
Keyword(s):  
Complex Systems ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Control Method ◽  
Hierarchical Control ◽  
System Level ◽  
Multidisciplinary Design ◽  
Control Parameters ◽  
Coupling Relationship ◽  
Design Variables

Because of the increasing complexity in engineering systems, multidisciplinary design optimization has attracted increasing attention. High computational expense and organizational complexity are two main challenges of multidisciplinary design optimization. To address these challenges, the hierarchical control method of complex systems is developed in this study. Hierarchical control method is a powerful way which has been utilized widely in the control and coordination of large-scale complex systems. Here, a hierarchical control method–based coupling relationship coordination algorithm is proposed to solve multidisciplinary design optimization problems. Coupling relationship coordination algorithm decouples the involved disciplines of a complex system and then optimizes each discipline objective at sub-system level. Coupling relationship coordination algorithm can maintain the consistency of interaction information (or in other words, sharing design variables and coupling design variables) in different disciplines by introducing control parameters. The control parameters are assigned by the coordinator at system level. A mechanical structure multidisciplinary design optimization problem is solved to illustrate the details of the proposed approach.

Application of Multidisciplinary Design Optimization to a Resource Satellite

2012 ◽  
Vol 195-196 ◽  
pp. 1066-1077
Author(s):  
Wen Rui Wu ◽  
Hai Huang ◽  
Bei Bei Wu
Keyword(s):  
Design Optimization ◽  
System Design ◽  
Multidisciplinary Design Optimization ◽  
Research Work ◽  
Thermal Control ◽  
Satellite System ◽  
Collaborative Optimization ◽  
System Level ◽  
Multidisciplinary Design ◽  
Satellite Mission

Satellite system design is a process involving various branches of knowledge, in which the designer usually needs to tradeoff many essentials and takes remarkable time. While multidisciplinary design optimization (MDO) method provides an effective approach for complicated system design, it seems especially suitable for such kind design purpose. By applying MDO in satellite system design, the efficiency of design can be expected to be improved and powerful technical supports can be obtained, which means better performance, faster design process and lower cost. According to the Resource satellite mission, width of ground cover and ground resolution are taken as the performance measurement, which combined with total mass of satellite is accounted in the optimization objective in system level. The design variables and constraints of the problem are dealt with disciplines or subsystems such as GNC, power, structure and thermal control. Corresponding analysis modules close to practical engineering are modeled. A MDO program system is developed by integrating collaborative optimization (CO) methods in iSIGHT. The result shows that the comprehensive objective can be improved, which also indicates MDO is feasible and efficient to solve the spacecraft design problem. The technology can be consulted for further research work.

Multidisciplinary Design Optimization on Conceptual Design of Aero-engine

2016 ◽  
Vol 33 (2) ◽  
Author(s):  
Xiao-bo Zhang ◽  
Zhan-xue Wang ◽  
Li Zhou ◽  
Zeng-wen Liu
Keyword(s):  
Design Optimization ◽  
Conceptual Design ◽  
Multidisciplinary Design Optimization ◽  
System Level ◽  
Multidisciplinary Design ◽  
Design Stage ◽  
Surface Model ◽  
Coupled Problem ◽  
Aero Engine ◽  
Integrated Performance

AbstractIn order to obtain better integrated performance of aero-engine during the conceptual design stage, multiple disciplines such as aerodynamics, structure, weight, and aircraft mission are required. Unfortunately, the couplings between these disciplines make it difficult to model or solve by conventional method. MDO (Multidisciplinary Design Optimization) methodology which can well deal with couplings of disciplines is considered to solve this coupled problem. Approximation method, optimization method, coordination method, and modeling method for MDO framework are deeply analyzed. For obtaining the more efficient MDO framework, an improved CSSO (Concurrent Subspace Optimization) strategy which is based on DOE (Design Of Experiment) and RSM (Response Surface Model) methods is proposed in this paper; and an improved DE (Differential Evolution) algorithm is recommended to solve the system-level and discipline-level optimization problems in MDO framework. The improved CSSO strategy and DE algorithm are evaluated by utilizing the numerical test problem. The result shows that the efficiency of improved methods proposed by this paper is significantly increased. The coupled problem of VCE (Variable Cycle Engine) conceptual design is solved by utilizing improved CSSO strategy, and the design parameter given by improved CSSO strategy is better than the original one. The integrated performance of VCE is significantly improved.

scholarly journals Multidisciplinary Design Optimization Of A Helicopter Rotor Blade

2021 ◽  
Author(s):  
Michael G. Leahy
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Rotor Blade ◽  
Vertical Shear ◽  
Multidisciplinary Design ◽  
Helicopter Rotor ◽  
Blade Element Theory ◽  
First Case ◽  
Design Variables ◽  
Helicopter Rotor Blade

Multidisciplinary design optimization (MDO) was performed on a helicopter rotor blade. The blade was modeled as a rigid flapping blade for dynamics; Blade Element Theory (BET) was the analysis approach to model the aerodynamic loading, and a simple linearly elastic hollowed rectangular section was the main structural component. MATLAB was used to solve the flapping differential equations and its Sequential Quadratic Programming (SQP) and Genetic Algorithm (GA) were used for the optimization. A Particle Swarm Optimization (PSO) routine was also tested. The optimization process consisted of three cases. The first case was a simple cantilever beam under centrifugal and an assumed bending loads. The optimization was performed using the SQP, GA, and PSO algorithms. The SQP resulted in the superior design with 75.45 compared to the GA's 87.1 and the PSO's 79.2, but a local minimum was present. The second case was an expansion of the first case by turning it into multidisciplinary problem. Aerodynamics was included in the design variables and objective function. Only the SQP algorithm was used and there was a reduction in hub vertical shear by 33.6%. The blade mass increased by 36.84%. The last case was an improvement to the second by creating a multiobjective problem by including the hub radial shear and the results were improved significantly by reducing the hub vertical shear by 34.06% and radial shear by 17.87% with a reduction of blade mass by 23.86%.

Multidisciplinary Design Optimization of Actuator Arrangements, Lay-Up Configurations and Control Systems for Smart Laminated Composite Structures

2010 ◽  
Author(s):  
Shinya Honda ◽  
Itsuro Kajiwara ◽  
Yoshihiro Narita
Keyword(s):  
Design Optimization ◽  
Control Systems ◽  
Multidisciplinary Design Optimization ◽  
Composite Structures ◽  
Vibration Suppression ◽  
Laminated Composite ◽  
Piezoelectric Actuators ◽  
Multidisciplinary Design ◽  
Design Variables ◽  
And Control

Structures and control systems of smart laminated composites consisting of graphite-epoxy composites and piezoelectric actuators are designed optimally for the vibration suppression. Placements of piezoelectric actuators, lay-up configurations of laminated composite plates and the H2 control system are employed as design variables and are optimized simultaneously by a simple genetic algorithm (SGA). An objective function is H2 performance with assuming that the state feedback is available. A multidisciplinary design optimization is performed with above three design variables and then the output feedback system is reconstructed with the dynamic compensator based on the linear matrix inequality (LMI) approach. Optimization results show that the optimized smart composite successfully realizes vibration suppression of the system and it is confirmed that the present multidisciplinary design optimization technique is quite efficient to the smart composites.

Hybrid reliability-based multidisciplinary design optimization with random and interval variables

2017 ◽  
Vol 232 (1) ◽  
pp. 52-64 ◽  
Author(s):  
Fan Yang ◽  
Zhufeng Yue ◽  
Lei Li ◽  
Dong Guan
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Region Of Interest ◽  
Kriging Model ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Learning Function ◽  
Design Variables ◽  
Interval Variables ◽  
Hybrid Reliability

This article presents a procedure for reliability-based multidisciplinary design optimization with both random and interval variables. The sign of performance functions is predicted by the Kriging model which is constructed by the so-called learning function in the region of interest. The Monte Carlo simulation with the Kriging model is performed to evaluate the failure probability. The sample methods for the random variables, interval variables, and design variables are discussed in detail. The multidisciplinary feasible and collaborative optimization architectures are provided with the proposed method. The method is demonstrated with three examples.

A Multidisciplinary Design Optimization Approach to Relating Affordability and Performance in a Conceptual Submarine Design

2010 ◽  
Vol 26 (04) ◽  
pp. 273-289 ◽  
Author(s):  
N. Vlahopoulos ◽  
C. G. Hart
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Geometric Model ◽  
Multidisciplinary Optimization ◽  
System Level ◽  
Multidisciplinary Design ◽  
Manufacturing Cost ◽  
Optimization Approach ◽  
And Performance ◽  
The Impact

A multidisciplinary design optimization (MDO) framework is used for a conceptual submarine design study. Four discipline-level performances—internal deck area, powering, maneuvering, and structural analysis—are optimized simultaneously. The four discipline-level optimizations are driven by a system level optimization that minimizes the manufacturing cost while at the same time coordinates the exchange of information and the interaction among the discipline-level optimizations. Thus, the interaction among individual optimizations is captured along with the impact of the physical characteristics of the design on the manufacturing cost. A geometric model for the internal deck area of a submarine is created, and resistance, structural design, and maneuvering models are adapted from theoretical information available in the literature. These models are employed as simulation drivers in the discipline-level optimizations. Commercial cost-estimating software is leveraged to create a sophisticated, automated affordability model for the fabrication of a submarine pressure hull at the system level. First, each one of the four discipline optimizations and also the cost-related top level optimization are performed independently. As expected, five different design configurations result, one from each analysis. These results represent the "best" solution from each individual discipline optimization, and they are used as reference for comparison with the MDO solution. The deck area, resistance, structural, maneuvering, and affordability models are then synthesized into a multidisciplinary optimization statement reflecting a conceptual submarine design problem. The results from this coordinated MDO capture the interaction among disciplines and demonstrate the value that the MDO system offers in consolidating the results to a single design that improves the discipline-level objective functions while at the same time produces the highest possible improvement at the system level.

Multidisciplinary Design Optimization of Small Canister-Launched Space Launch Vehicle Using Genetic Algorithm

2013 ◽  
Vol 302 ◽  
pp. 583-588 ◽  
Author(s):  
Fredy M. Villanueva ◽  
Lin Shu He ◽  
Da Jun Xu
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimal Solution ◽  
Optimization Method ◽  
Launch Vehicle ◽  
Low Earth Orbit ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Design Variables

A multidisciplinary design optimization approach of a three stage solid propellant canister-launched launch vehicle is considered. A genetic algorithm (GA) optimization method has been used. The optimized launch vehicle (LV) is capable of delivering a microsatellite of 60 kg. to a low earth orbit (LEO) of 600 km. altitude. The LV design variables and the trajectory profile variables were optimized simultaneously, while a depleted shutdown condition was considered for every stage, avoiding the necessity of a thrust termination device, resulting in reduced gross launch mass of the LV. The results show that the proposed optimization approach was able to find the convergence of the optimal solution with highly acceptable value for conceptual design phase.

Performance Measure Approach Based Multidisciplinary Design Optimization of Gear Transmission

2013 ◽  
Vol 694-697 ◽  
pp. 868-871
Author(s):  
Jun Zhang ◽  
Bing Zhang
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Performance Measure ◽  
Collaborative Optimization ◽  
Gear Transmission ◽  
System Level ◽  
Multidisciplinary Design ◽  
Engineering Systems ◽  
Performance Measure Approach ◽  
Design Cost

In order to reduce the influence of uncertainties on complicated engineering systems performance, a new method is proposed based on the performance measure approach and collaborative optimization (PMA-CO) to implement the reliability-based multidisciplinary design optimization of gear transmission. Both the mathematical model and procedures of PMA-CO are presented. With the adoption of slack factors in the system-level of collaborative optimization, both CO and PMA-CO are applied to the optimization of gear transmission. The proposed PMA-CO improves the reliability of the gear transmission and gained a tradeoff solution between design cost and reliability. Therefore, the PMA-CO is effective and practical in engineering design.

Application of the multidisciplinary design optimization algorithm to the design of a belt-integrated seat while considering crashworthiness

2005 ◽  
Vol 219 (11) ◽  
pp. 1281-1292 ◽  
Author(s):  
M K Shin ◽  
B S Kang ◽  
G J Park
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
System Analysis ◽  
Steering System ◽  
Multidisciplinary Design ◽  
Convergence Criterion ◽  
Design Variables ◽  
Design Values ◽  
Subspace Design ◽  
Energy Absorbing

Multidisciplinary design optimization based on independent subspaces (MDOIS), which is a multidisciplinary design optimization (MDO) algorithm, has been recently proposed. Since MDOIS is relatively simple compared with other MDO algorithms, it is easy to apply MDOIS to practical engineering problems. In this research, an MDO problem is defined for the design of a belt-integrated seat (BIS) while considering crashworthiness. The crash model consists of an airbag, a BIS, an energy-absorbing steering system, and a safety belt. It is found that the current design problem has two disciplines - structural non-linear analysis and occupant analysis. The interdisciplinary relationship between the disciplines is identified. Interdisciplinary variables between the two disciplines are stiffness of the seat back frame and the belt load. The interdisciplinary relationship is addressed in the system analysis step in MDOIS. Prior to each independent subspace design, values of interdisciplinary variables at a given design point are determined in the system analysis step. The determined values are passed to corresponding subspaces, and the subspaces treat the received values of the interdisciplinary variables as constant parameters throughout the subspace design. For the present example, the belt load is passed to the structural analysis subspace and the stiffness of the seat back frame is passed to the occupant analysis subspace. Determined design variables in each subspace are passed to the system analysis step. In this way, the design process iterates until the convergence criterion is satisfied. As a result of the design, the weight of the BIS and the head injury criterion (HIC) of an occupant are reduced while the specified constraints are satisfied. Since the system analysis cannot be formulated in an explicit form in the current example, an optimization problem is formulated to solve the system analysis. The results from MDOIS are discussed.

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