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The capability to predict and evaluate various configurations’ performance during the conceptual design phase using multidisciplinary design analysis and optimization can significantly increase the preliminary design process’s efficiency and reduce design and development costs. This research paper aims to perform multidisciplinary design and optimization for an expendable microsatellite launch vehicle (MSLV) comprising three solid-propellant stages, capable of delivering micro-payloads in the low earth orbit. The methodology’s primary purpose is to increase the conceptual and preliminary design process’s efficiency by reducing both the design and development costs.

Multidiscipline feasible architecture is applied for the multidisciplinary design and optimization of an expendable MSLV at the conceptual level to accommodate interdisciplinary interactions during the optimization process. The multidisciplinary design and optimization framework developed and implemented in this research effort encompasses coupled analysis disciplines of vehicle geometry, mass calculations, aerodynamics, propulsion and trajectory. Nineteen design variables were selected to optimize expendable MSLV to launch a 100 kg satellite at an altitude of 600 km in the low earth orbit. Modern heuristic optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO) and SA are applied and compared to obtain the optimal configurations. The initial population is created by passing the upper and lower bounds of design variables to the optimizer. The optimizer then searches for the best possible combination of design variables to obtain the objective function while satisfying the constraints.

All of the applied heuristic methods were able to optimize the design problem. Optimized design variables from these methods lie within the lower and upper bounds. This research successfully achieves the desired altitude and final injection velocity while satisfying all the constraints. In this research effort, multiple runs of heuristic algorithms reduce the fundamental stochastic error.

The use of multiple heuristics optimization methods such as GA, PSO and SA in the conceptual design phase owing to the exclusivity of their search approach provides a unique opportunity for exploration of the feasible design space and helps in obtaining alternative configurations capable of meeting the mission objectives, which is not possible when using any of the single optimization algorithm.

The optimized configurations can be further used as baseline configurations in the microsatellite launch missions’ conceptual and preliminary design phases.

Satellite launch vehicle design and optimization is a complex multidisciplinary problem, and it is dealt with effectively in the multidisciplinary design and optimization domain. It integrates several interlinked disciplines and gives the optimum result that satisfies these disciplines’ requirements. This research effort provides the multidisciplinary design and optimization-based simulation framework to predict and evaluate various expendable satellite launch vehicle configurations’ performance. This framework significantly increases the conceptual and preliminary design process’s efficiency by reducing design and development costs.

The purpose of this paper is to present an application of a multidisciplinary multi-level design optimization methodology for the optimal design of a complex device from the field of electrical engineering throughout discipline-based decomposition. The considered benchmark is a single-phase low voltage safety isolation transformer.

The multidisciplinary optimization of a safety isolation transformer is addressed within this paper. The bi-level collaborative optimization (CO) strategy is employed to coordinate the optimization of the different disciplinary analytical models of the transformer (no-load and full-load electromagnetic models and thermal model). The results represent the joint decision of the three distinct disciplinary optimizers involved in the design process, under the coordination of the CO's master optimizer. In order to validate the proposed approach, the results are compared to those obtained using a classical single-level optimization method – sequential quadratic programming – carried out using a multidisciplinary feasible formulation for handling the evaluation of the coupling model of the transformer.

Results show a good convergence of the CO process with the analytical modeling of the transformer, with a reduced number of coordination iterations. However, a relatively important number of disciplinary models evaluations were required by the local optimizers.

The CO multi-level methodology represents a new approach in the field of electrical engineering. The advantage of this approach consists in that it integrates decisions from different teams of specialists within the optimal design process of complex systems and all exchanges are managed within a unique coordination process.

As an advanced calculation methodology, reliability-based multidisciplinary design optimization (RBMDO) has been widely acknowledged for the design problems of modern complex engineering systems, not only because of the accurate evaluation of the impact of uncertain factors but also the relatively good balance between economy and safety of performance. However, with the increasing complexity of engineering technology, the proposed RBMDO method gradually cannot effectively solve the higher nonlinear coupled multidisciplinary uncertainty design optimization problems, which limits the engineering application of RBMDO. Many valuable works have been done in the RBMDO field in recent decades to tackle the above challenges. This study is to review these studies systematically, highlight the research opportunities and challenges, and attempt to guide future research efforts.

This study presents a comprehensive review of the RBMDO theory, mainly including the reliability analysis methods of different uncertainties and the decoupling strategies of RBMDO.

First, the multidisciplinary design optimization (MDO) preliminaries are given. The basic MDO concepts and the corresponding mathematical formulas are illustrated. Then, the procedures of three RBMDO methods with different reliability analysis strategies are introduced in detail. These RBMDO methods were proposed for the design optimization problems under different uncertainty types. Furtherly, an optimization problem for a certain operating condition of a turbine runner blade is introduced to illustrate the engineering application of the above method. Finally, three aspects of future challenges for RBMDO, namely, time-varying uncertainty analysis; high-precision surrogate models, and verification, validation and accreditation (VVA) for the model, are discussed followed by the conclusion.

The scope of this study is to introduce the RBMDO theory systematically. Three commonly used RBMDO-SORA methods are reviewed comprehensively, including the methods' general procedures and mathematical models.

A method to solve shape and size optimisation problems with linear and non‐linear responses has been studied taking advantage of statistical methodologies. A nested optimisation procedure has been fixed. The global optimisation problem is decomposed in several subproblems where each non‐linear response is locally approximated with a first degree polynomial function identified by the definition and execution of an experimental plan. The approximating functions so obtained are used to evaluate the design sensitivity coefficients required by the optimisation procedure. The numerical results obtained during the optimisation process to verify exactly the value of the non‐linear responses are used to verify and to improve the approximating function accuracy. The non‐linear design sensitivity analysis method so defined has been used to solve a multidisciplinary shape optimisation problem involving a real 3D automotive structure.

The purpose of this paper is a development of a virtual flight test framework with derivative design optimization. Aircraft manufactures and engineers have been putting significant effort into the design process to lower the cost of development and time to a minimum. In terms of flight tests and aircraft certification, implementing simulation and virtual test techniques may be a sufficient method in achieving these goals. In addition to simulation and virtual test, a derivative design can be implemented to satisfy different market demands and technical changes while reducing development cost and time.

In this paper, a derivative design optimization was applied to Expedition 350, a small piston engine powered aircraft developed by Found Aircraft in Canada. A derivative that changes the manned aircraft to an Unmanned Aerial Vehicle for payload delivery was considered. An optimum configuration was obtained while enhancing the endurance of the UAV. The multidisciplinary design optimization module of the framework represents the optimized configuration and additional parameters for the simulator. These values were implemented in the simulator and generated the aircraft model for simulation. Two aircraft models were generated for the flight test.

The optimization process delivered the UAV derivative of Expedition E350, and it had increased endurance up to 21.7 hours. The original and optimized models were implemented into virtual flight test. The cruise performance exhibited less than 10 per cent error on cruise performance between the original model and Pilots Operating Handbook (POH). The dynamic stability of original and optimized models was tested by checking Phugoid, short period, Dutch roll and spiral roll modes. Both models exhibited stable dynamic stability characteristics.

The original Expedition 350 was generated to verify the accuracy of the simulation data by comparing its result with actual flight test data. The optimized model was generated to evaluate the optimization results. Ultimately, the virtual flight test framework with an aircraft derivative design was proposed in this research. The additional module for derivative design optimization was developed and its results were implemented to commercial off-the-shelf simulators.

This paper proposed the application of UAV derivative design optimization for the virtual flight test framework. The methodology included the optimization of UAV derivative utilizing MDO and virtual flight testing of an optimized result with a flight simulator.

The purpose of this paper is to solve the common problems that traditional optimization methods cannot fully improve the performance of electromagnetic linear actuators (EMLAs).

In this paper, a multidisciplinary optimization (MDO) method based on the non-dominated sorting genetic algorithm-II (NSGA-II) algorithm was proposed. An electromagnetic-mechanical coupled actuator analysis model of EMLAs was established, and the coupling relationship between static/dynamic performance of the actuator was analyzed. Suitable optimization variables were designed based on fuzzy grayscale theory to address the incompleteness of the actuator data and the uncertainty of the coupling relationship. A multiobjective genetic algorithm was used to obtain the optimal solution set of Pareto with the maximum electromagnetic force, electromagnetic force fluctuation rate, time constant and efficiency as the optimization objectives, the final optimization results were then obtained through a multicriteria decision-making method.

The experimental results show that the maximum electromagnetic force, electromagnetic force fluctuation rate, time constants and efficiency are improved by 18.1%, 38.5%, 8.5% and 12%, respectively. Compared with single-discipline optimization, the effectiveness of the multidiscipline optimization method was verified.

This paper proposes a MDO method for EMLAs that takes into account static/dynamic performance, the proposed method is also applicable to the design and analysis of various electromagnetic actuators.

The purpose of this paper is to present application of multidisciplinary design optimisation (MDO) in redesign of a small composite aircraft. The redesign process was integration of the turboprop engine in a small composite aircraft. The process requires cooperation of specialists from many disciplines and definition of their tasks. For selected tasks, the authors present results of the calculation.

The authors used collaborative optimisation (CO) algorithm to solve the problem. They decomposed this complex process into a set of tasks in different engineering/research disciplines and used techniques and methods specific for each task (research/engineering discipline) to find a proper solution. The computer-aided design (CAD), computational fluid dynamics (CFD) and computational structural mechanics (CSM) commercial software were used as common tools as well as intentionally developed computer programmes were used as basic tools in some tasks, in particular, for aerodynamic optimisation, calculation of load and stability of aircraft. The exchange of data between separate tasks allowed achieving the main goal of complex design process.

Selected optimisation algorithm, CO, proved efficient for the authors’ purposes. The effectiveness of multidisciplinary optimisation depends as much on organisational parameters as it does on technical and technology parameters.

Multidisciplinary optimisation needs to be an integral part of analysis and design process. The successful optimisation results allowed to meet the requirements and to proceed to the next phase of work – preparing technical documentation for manufacturing the components necessary for integration of the airplane with the new engine.

Presented results of design process are a valuable example of how to achieve the final goal in an ongoing project.

The purpose of this paper is to study the consideration of uncertainty from analysis modules for aircraft conceptual design by implementing uncertainty-based design optimization methods. Reliability-Based Design Optimization (RBDO), Possibility-Based Design Optimization (PBDO) and Robust Design Optimization (RDO) methods were developed to handle uncertainties of design optimization. The RBDO method is found suitable for uncertain parameters when sufficient information is available. On the other hand, the PBDO method is proposed when uncertain parameters have insufficient information. The RDO method can apply to both cases. The RBDO, PBDO and RDO methods were considered with the Multidisciplinary Design Optimization (MDO) method to generate conservative design results when low fidelity analysis tools are used.

Methods combining MDO with RBDO, PBDO and RDO were developed and have been applied to a numerical analysis and an aircraft conceptual design. This research evaluates and compares the characteristics of each method in both cases.

The RBDO result can be improved when the amount of data concerning uncertain parameters is increased. Conversely, increasing information regarding uncertain parameters does not improve the PBDO result. The PBDO provides a conservative result when less information about uncertain parameters is available.

The formulation of RDO is more complex than other methods. If the uncertainty information is increased in aircraft conceptual design case, the accuracy of RBDO will be enhanced.

This research increases the probability of a feasible design when it considers the uncertainty. This result gives more practical optimization results on a conceptual design level for fabrication.

It is RBDO, PBDO and RDO methods combined with MDO that satisfy the target probability when the uncertainties of low fidelity analysis models are considered.

The purpose of this paper is the optimal design of a reentry vehicle configuration to minimize the mission cost which is equal to minimize the heat absorbed (thermal protection system mass) and structural mass and to maximize the drag coefficient (trajectory errors and minimum final velocity).

There are two optimization approaches for solving this problem: multiobjective optimization (lead to Pareto optimal solutions); and single‐objective optimization (lead to one optimal solution). Single‐objective genetic algorithms (GA) and multiobjective Genetic Algorithms (MOGA) are employed for optimization. In second approach, if there are n objectives (n+1) GA run is needed to find nearest point (optimum point), which leads to increase the time processing. Thus, a modified GA called single run GA (SRGA) is presented as third approach to avoid increasing design time. It means if there are n objectives, just one GA run is enough.

Two multi module function – Ackley and bump function – are selected for examination the third approach. Results of MOGA, GA and SRGA are presented which show SRGA approach can find the nearest point in much shorter time with acceptable accuracy.

GA, MOGA and SRGA approaches are applied to multidisciplinary design optimization of a reentry vehicle configuration and results show the efficiency of SRGA in complex design optimization problem.

The paper aims to apply numerical optimization to the aircraft design procedures applied in the airspace industry.

It is harder than ever to achieve competitive construction. This is why numerical optimization is becoming a standard tool during the design process. Although optimization procedures are becoming more mature, yet in the industry practice, fairly simple examples of optimization are present. The more complicated is the task to solve, the harder it is to implement automated optimization procedures. This paper presents practical examples of optimization in aerospace sciences. The methodology is discussed in the article in great detail.

Encountered problems related to the numerical optimization are presented. Different approaches to the solutions of the problems are shown, which have impact on the time of optimization computations and quality of the obtained optimum. Achieved results are discussed in detail with relation to the used settings.

Investigated different aspects of handling optimization problems, improving quality of the obtained optimum or speeding-up optimization by parallel computations can be directly applied in the industry optimization practice. Lessons learned from multidisciplinary optimization can bring industry products to higher level of performance and quality, i.e. more advanced, competitive and efficient aircraft design procedures, which could be applied in the industry practice. This can lead to the new approach of aircraft design process.

Introduction of numerical optimization methods in aircraft design process. Showing how to solve numerical optimization problems related to advanced cases of conceptual and preliminary aircraft design.