Aircraft Multidisciplinary Design Optimization Under Both Model and Design Variables Uncertainty

2013 ◽  
Vol 50 (2) ◽  
pp. 528-538 ◽  
Author(s):  
L. Jaeger ◽  
C. Gogu ◽  
S. Segonds ◽  
C. Bes
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Multidisciplinary Design ◽  
Design Variables

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.

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.

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.

Knowledge-Based Aero-Thermal Multi-Disciplinary Design Optimization of a High Temperature Blade

2017 ◽  
Author(s):  
Yingjie Song ◽  
Zhendong Guo ◽  
Liming Song ◽  
Jun Li ◽  
Zhenping Feng
Keyword(s):  
High Temperature ◽  
Design Optimization ◽  
Thermal Performance ◽  
Multidisciplinary Design Optimization ◽  
Differential Evolution Algorithm ◽  
Heat Transfer Analysis ◽  
Multidisciplinary Design ◽  
Pareto Solutions ◽  
Knowledge Based ◽  
Design Variables

The multidisciplinary design optimization of high temperature blades is a typical high dimensional, computational expensive and black box problem, since too many design variables are involved and large amounts of CFD evaluations are usually demanded to ensure the convergence of global algorithms like GAs. By integrating the technique of analysis of variance (ANOVA), Self-adaptive Multi-objective Differential Evolution algorithm (SMODE), Conjugate Heat Transfer analysis and 3D parameterization method for both blade and the cooling holes, a knowledge-based aero-thermal multidisciplinary design optimization of a high temperature blade is carried out. Through the ANOVA analysis, an insight into the relation between significant design variables and the blade aero-thermal performance is obtained. By eliminating the variables which have small effects on the blade aero-thermal performance, the number of design variables for the optimization process is decreased from 36 to 15, which is verified by the numerical simulations. After optimization, 9 optimal Pareto solutions are achieved. Detailed aero-thermal analysis of typical optimal Pareto solutions indicates that the performance of optimal designs is significantly better than the reference design. Therefore, the effectiveness of the developed knowledge-based multidisciplinary design method for high temperature blades is demonstrated.

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 Approach for a Small Solid Propellant Launch Vehicle Conceptual Design Using Hybrid Simulated Annealing

2011 ◽  
Vol 110-116 ◽  
pp. 4765-4771 ◽  
Author(s):  
Masoud Ebrahimi ◽  
Jafar Roshanian ◽  
Farnaz Barzinpour
Keyword(s):  
Simulated Annealing ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Solid Propellant ◽  
Launch Vehicle ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Design Variables ◽  
Base Method ◽  
Hybrid Simulated Annealing

Multidisciplinary Design Optimization (MDO) of a two-stage Small Solid Propellant Launch Vehicle (SSPLV) by simulated annealing (SA) Method is investigated. Propulsion, weight, aerodynamic (geometry) and 3degree of freedom (3DOF) trajectory simulation disciplines are used in an appropriate combination. Suitable design variables, technological-functional constraints and minimum launch weight objective function are considered. To handle constraints augmentation of constraints to cost using penalty coefficients are used. Results are compared with gradient-base method that shows the ability of SA to escape local optimums.

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.

A new multidisciplinary robust design optimization framework for an autonomous underwater vehicle in system and tactic design

2018 ◽  
Vol 233 (3) ◽  
pp. 918-936
Author(s):  
Mohsen Bidoki ◽  
Mehdi Mortazavi ◽  
Mehdi Sabzehparvar
Keyword(s):  
Design Optimization ◽  
Design Process ◽  
Robust Design ◽  
Multidisciplinary Design Optimization ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Robust Design Optimization ◽  
Multidisciplinary Design ◽  
Optimization Framework ◽  
Design Variables

The design process of an autonomous underwater vehicle requires mathematical model of subsystems or disciplines such as guidance and control, payload, hydrodynamic, propulsion, structure, trajectory and performance and their interactions. In early phases of design, an autonomous underwater vehicle is often encountered with a high degree of uncertainty in the design variables and parameters of system. These uncertainties present challenges to the design process and have a direct effect on the autonomous underwater vehicle performance. Multidisciplinary design optimization is an approach to find both optimum and feasible design, and robust design is an approach to make the system performance insensitive to variations of design variables and parameters. It is significant to integrate the robust design and the multidisciplinary design optimization for designing complex engineering systems in optimal, feasible and robust senses. In this article, we present an improved multidisciplinary design optimization methodology for conceptual design of an autonomous underwater vehicle in both engineering and tactic aspects under uncertainty. In this methodology, uncertain multidisciplinary feasible is introduced as uncertain multidisciplinary design optimization framework. The results of this research illustrate that the new proposed robust multidisciplinary design optimization framework can carefully set a robust design for an autonomous underwater vehicle with coupled uncertain disciplines.

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