In recent years, the use of air taxis as a suitable solution for transporting cargo and passengers, has been considered especially in short distances and in the city. Complex systems, such as air taxis, are involved in several subsystems with interacting and sometimes conflicting effects are difficult to be derived. Modern optimal design methods such as Multidisciplinary design optimization can derive the optimal design while satisfying all the constraints and limitation. In this article, Multidisciplinary design optimization of an air taxi is discussed. The optimization framework is selected based on AAO by considering structure, aerodynamics, flight mechanics, propulsion and electrical power. Total mass of air taxi is selected as cost function. Finally, the optimal results are compared and evaluated with the results of two classical design methods including "weight estimation" and " sensitivity of design coefficients". The results confirm the improvement of optimal solution with compare of classical methods.

This paper presents an efficient global meta-model building technique for solving high fidelity Multidisciplinary design optimization (MDO) problems. The main difficulties associated with MDO are often characterized by interdisciplinary couplings, high computational cost of an analysis in individual disciplines and a large number of design variables and constraints. These issues result in very high overall computational cost limiting applications of MDO to complex industrial design problems. To address these issues a combination of global meta-model using moving least squares (MLSM) and the trust region strategy is introduced. A global meta-model is used to identify the feasible and infeasible regions and the trust region strategy is used for a detailed search of the feasible region. The technique is demonstrated on a test problem and the effectiveness of the method for modeling and system level collaborative optimization using high fidelity models is studied. The results show that meta-model based on MLSM provide a high degree of accuracy whilst achieving a considerable reduction in computational cost.

This research tries to propose a method to solve problems related to constrained multi-objective optimizations (implementation, computation time, and simplicity). This method, based on fuzzy logic, converts constrained multi-objective optimization problem into unconstrained single-objective optimization problems so many of the mentioned problems are solved. To demonstrate the efficiency of this method, three Multidisciplinary design optimizations of an unmanned aerial vehicle have been performed. The aim of the first optimization is to compare the performance of the proposed method with two well-known methods of multi-objective optimizations. The purpose of the second and third optimizations is to show this capability of the proposed method that the designer, according to need, can consciously change the degree of importance on the objective functions or constraints. The results of the optimizations show that the computational time has been reduced, and two different optimal designs have been obtained by changing the degree of importance.

The purpose of this article is the conceptual design of a two-stage crew launch vehicle with side boosters. At first, in order to achieve a suitable design point, the statistical design technique is used, and then statistical design process is validated by using two degree of freedom simulation and doing energetic-mass calculations. Then, Multidisciplinary design optimization approach is applied for initial conceptual design optimization. The preferred structure for Multidisciplinary design optimization is all-at-once and Genetic Algorithm (GA) is used as the optimizer algorithm. In order to achieve more accuracy, Simplex method is employed using GA’s results as a combined algorithm. Having performed the optimization process, a mass decrease of about 4 tons from missile gross weight was attained with respect to normal simulation results, and as already known, the decrease in gross mass undeniably leads to a consequent decrease in the cost of producing and launching missiles.

The design process of an Autonomous Underwater Vehicle (AUV) 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 AUV are 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 AUV performance. Multidisciplinary design optimization (MDO) 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 robust design and MDO for designing complex engineering systems in optimal, feasible and robust senses. In this paper, an improved robust MDO methodology is developed for conceptual design of an AUV under uncertainty with considering tactic and system design simultaneously. In this methodology, Uncertain Multi-Disciplinary Feasible (UMDF) framework is introduced as uncertain MDO framework. Two evolutionary algorithms are also used as Pareto-based Multi-Objective optimizers and results of two algorithms are compared. The results of this research illustrate that the new proposed robust Multidisciplinary design optimization framework can carefully set a robust design for an AUV with coupled uncertain disciplines.

This article provides Multidisciplinary design software for design of a class of General Aviation Aircrafts (GAAs). In the developed software, disciplines such as engine selection, weight and sizing, aerodynamics, performance and stability have been integrated together in Multidisciplinary design Feasibility (MDF) structure. At the beginning of the design process of this software, preliminary aircraft configuration will determined based on a preset series of requirements and statistical study. Afterwards, the MDF loop by implementing a Multidisciplinary analysis assesses the design in the presence of performance and mission constraints. The constraints and algorithms that are considered in the design process are based on the Gudmundsson design approach. design variables are selected carefully using sensitivity analysis on design objectives (i. e. reducing the gross weight and increasing the range). In order to obtain a feasible design, static stability constraints are considered. Finally, the multi-objective evolutionary optimization algorithm (NSGA-II) is utilized to demonstrate a set of possible answers in the form of Pareto frontier. This software has the ability to add a variety of engines and airfoils, will cover a comprehensive range of optimal designs. The Pareto fronts resulted from optimization process illustrates the feasibility and effectiveness of this conceptual design software.

There is no determined method for Micro Air Vehicles (MAVs) design (unlike full-scale aircraft), so MAVs design is very complex and vague. For this reason, the design of MAVs is very expensive (time-consuming), and finally, the obtained design could not be more optimal. To solve these challenges, this study developed a framework for Multidisciplinary design optimization (MDO) of fixed-wing MAVs. This framework aims to use the benefits of MDO (time reduction and achieving optimal design) in the design process of MAVs. So, it is tried to consider the most important modules for analysis, and the framework can consider all flight phases in the design optimization process. Geometry, weight, the center of gravity, aerodynamics, and power are the considered modules in this framework. The analysis of all modules is performed for the entire flight phase. To show the performance of this framework, the design optimization of a fixed-wing MAV has been done by considering take-off weight and drag as objective functions.  The considered constraints for this research are from stability and geometry modules. It is worth noting that with attention to the complex design space of MAVs and the capability of the Genetic Algorithm (GA), this algorithm has been considered as an optimization algorithm in this study.

Considering the importance of the presence of uncertainties in the design of complex engineering systems, in this research Multidisciplinary design optimization process for a bipropellant propulsion system in the presence of uncertainties, which in addition to minimizing the system mass, has a high robust. Based on this, the Multidisciplinary design view of the bipropellant propulsion system is expressed in both optimum design and optimum robust design. The continued with the application of uncertainties, the mass, operational and geometric results of the propulsion system are expressed in terms of optimum design, robust design and optimum robust design. According to the results, it is shown that the lowest mass occurs in optimum design mode. But with uncertainties, it is observed at this point that it has the least robust and reliability. It also attempts to explain the difference between the concepts of robust design and optimum design with the help of results.

Complex systems design problems entail a suitable structure in which all disciplines, including their coupled relationships, have been considered and modeled at the same time. These types of design problems involve time and computational cost challenges. Multi-Disciplinary design optimization (MDO) methods have been developed to address these issues simultaneously. This article aims to provide a proper design structure for an uncertainty-based re-entry trajectory design optimization problem under the control restrictions and structural constraints of a Reusable Flexible Space Launch Vehicle (RFSLV) alongside the determination of optimal skin thickness and thermal protection system thickness concerning the design criteria of the flexible structure in such a way that the final design would meet the desired reliability. Trajectory, structure, aerodynamics, aeroelasticity, and thermal protection systems are considered to be involved disciplines in the design problem. The study's purpose will be to obtain an optimal trajectory to meet all the control and structure restrictions while estimating optimal body skin and thermal protection thicknesses based on structural design criteria evaluating the re-entry trajectory, which is in process. The flexible space launcher body has been considered as a free-free Bernoulli-Euler beam for bending variation and D’alembert’s principle for inertia force in static model with the aim of assessing structural design standards. The 3DOF longitudinal dynamic equations plus the first bending mode have been considered. By Chebyshev polynomial interpolation, the angle of attack scope has been achievable, and then the trajectory optimization problem has been transformed to a discrete nonlinear programming problem (NLP), which leads to numerical integration of state equation and satisfying all path constraints in Bolza optimal control problem. All highly nonlinear uncertainty-based constraints in the model have led to taking advantage of the evolutionary optimization algorithm that has been implemented here by Non-Dominated Sorting Genetic Algorithm (NSGA-II). Finally, epistemic and aleatory uncertainties have been applied through Probability Theory to estimate the reliability of constraints that had been affected by uncertainties. The result shows a 75 percent decrease through utilizing the evolutionary multi-objective technique against the gradient-based algorithm in design space optimization regarding computational cost in recalling objective functions. The other conclusion is that the sequential reliability analysis structure modeling efficiency is much better compared to the parallel one.

In this paper the problem of Multidisciplinary and multi-objective conceptual design optimization of an air launched projectile (ALP) is investigated. The proposed task is performed using a three degree of freedom (DOF) flight dynamics simulation model and taking into account all the constraints involved in the optimization process. To maximize the payload weight as well as the range of ALP, the vehicle weight and balance, aerodynamics and stability disciplines are selected in the optimization process using a model with moderate levels of fidelity for each subject. The ALP design optimization problem contains 14 design variables and 2 target functions that include the payload weight and the vehicle range. Finally, a performance based comparison of results between the optimized ALP and its non-optimum initial configuration has been made. In addition, a Monte Carlo analysis is performed over the optimal ALP design to see the effects of launching uncertainties in meeting the mission requirements.