Application Of Multidisciplinary Design Optimization Research Articles

A fluid–structure analysis approach and its application in the uncertainty-based multidisciplinary design and optimization for blades

In practical engineering, the choice of blade shape is crucial in the design process of turbine. It is because not only the structural stability but also the aerodynamic performance of turbine depends on the shape of blades. Generally, the design of blades is a typical multidisciplinary design optimization problem which includes many different disciplines. In this study, a fluid–structure coupling analysis approach is proposed to show the application of multidisciplinary design optimization in engineering. Furthermore, a strategy of uncertainty-based multidisciplinary design optimization using fluid–structure coupling analysis is proposed to enhance the reliability and safety of blades in turbine. The design of experiment technique is also introduced to construct response surface during uncertainty-based multidisciplinary design optimization using fluid–structure coupling analysis. The design solution shows that the adiabatic efficiency is increased and the equivalent stress is decreased, which means that better performance of the turbine can be obtained.

Multidisciplinary Design Optimization of Aerial Vehicles: A Review of Recent Advancements

The aim of this paper is to present the most common practices in multidisciplinary design optimization (MDO) of aerial vehicles over the past decade. The literature sample is identified through established internet search engines, and a stringent review methodology is implemented in order to ensure the selection of the most relevant sources. In this work, the primary emphasis is on the assessment of the state-of-the-art framework development strategies, while at a secondary level, the objective is to identify the possible improvement directions by evaluating the research trends and gaps. As an additional contribution, statistical studies are also provided, and it is shown how MDO of aerial vehicles has evolved in terms of problem formulation, disciplinary modeling, analysis capabilities, tool implementation, and general applicability. Given this foundation as well as the results of the review, this work concludes by presenting a roadmap for guiding academia and industry in respect to the application of MDO on aerial vehicles. Overall, the roadmap together with the literature review is not only expected to serve as a guide for newcomers into the MDO field but also as an elementary basis which will allow researchers to conduct additional studies in this important and constantly evolving area of design.

Application of Multidisciplinary Design Optimization on Advanced Configuration Aircraft

An optimization strategy is constructed to solve the aerodynamic and structural optimization problems in the conceptual design of double-swept flying wing aircraft. Aircraft preliminary aerodynamic and structural design optimization is typically based on the application of a deterministic approach of optimizing aerodynamic performance and structural weight. In aerodynamic optimization, the objective is to minimize induced drag coefficient, and the structural optimization aims to find the minimization of the structural weight. In order to deal with the multiple objective optimization problems, an optimization strategy based on collaborative optimization is adopted. Based on the optimization strategy, the optimization process is divided into system level optimization and subsystem level optimization. The system level optimization aims to obtain the optimized design which meets the constraints of all disciplines. In subsystem optimization, the optimization process for different disciplines can be executed simultaneously to search for the consistent schemes. A double-swept configuration of flying wing aircraft is optimized through the suggested optimization strategy, and the optimization results demonstrate the effectiveness of the method.

Application of Multidisciplinary Design Optimization on Advanced Configuration Aircraft

Application of Multidisciplinary Design Optimization on Advanced Configuration Aircraft

ESTABLISHMENT OF EFFECTIVE METAMODELS FOR SEAKEEPING PERFORMANCE IN MULTIDISCIPLINARY SHIP DESIGN OPTIMIZATION

Ship design is a complex multidisciplinary optimization process to determine configuration variables that satisfy a set of mission requirements. Unfortunately, high fidelity commercial software for the ship performance estimation such as Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) is computationally expensive and time consuming to execute and deters the ship designer’s ability to explore larger range of optimization solutions. In this paper, the Latin Hypercube Design was used to select the sample data for covering the design space. The percentage of downtime, a comprehensive seakeeping evaluation index, was also used to evaluate the seakeeping performance within the short-term and long-term wave distribution in the process of Multidisciplinary Design Optimization (MDO). The five motions of ship seakeeping performance contained roll, pitch, yaw, sway and heave. Particularly, a new effective approximation modelling technique-Single-Parameter Lagrangian Support Vector Regression (SPL-SVR) was investigated to construct ship seakeeping metamodels to facilitate the application of MDO. By considering the effects of two ship speeds, the established metamedels of ship seakeeping performance for the short-term percentage of downtime are satisfactory for seakeeping predictions during the conceptual design stage; thus, the new approximation algorithm provides an optimal and cost-effective solution for constructing the metamodels in MDO process.

An application of multidisciplinary design optimization to the hydrodynamic performances of underwater robots

Multidisciplinary Design Optimization (MDO) is proposed for the design of the lines of an underwater robot. Hydrodynamic performances of the underwater robot are concerned about in the design, including the resistance and the manoeuvrability. A method of MDO, Collaborative Optimization (CO), is adopted. To improve the efficiency of optimization, approximate models are established in sub-disciplines. Also, an artificial intelligent technique, Particle Swarm Optimization (PSO), is incorporated into the CO framework. The optimization design of the lines of the underwater robot is carried out on an Isight platform, an automatic integration optimization platform. Through the platform, the optimization design of the lines and the analysis of the hydrodynamic performances can be achieved automatically with high efficiency. An autonomous underwater vehicle (AUV), SUBOFF, is taken as a verification model. For different lines, CFD calculation is performed to analyze the resistance and manoeuvrability. By comparison, the optimal lines of the hull and the fairwater are determined.

Evolutionary energy performance feedback for design: Multidisciplinary design optimization and performance boundaries for design decision support

In pursuit of including energy performance as feedback for architects’ early stage design decision making, this research presents the theoretical foundation of a designer oriented multidisciplinary design optimization (MDO) framework titled evolutionary energy performance feedback for design (EEPFD). Through a comprehensive literature review and gap analysis EEPFD is developed into an MDO methodology that provides energy performance as feedback for influencing architects’ decision making more fluidly and earlier than other approaches to date. Secondly, in response to the lack of an MDO best practice EEPFD is investigated and evaluated through two experiments. The first experiment demonstrates the ability to utilize EEPFD provided energy performance as feedback to pursue multiple architectural designs with competing objectives and tradeoffs. The second experiment identifies performance boundaries as a best practice for MDO applications to the early stage architectural design processes. The research synthesizes the results into the basis for measuring these performance boundaries as a best practice in the context where architects must gauge multiple design concepts with varying complexity coupled with performance objectives through EEPFD, thereby enhancing the influence of energy performance feedback on the early stage design process. Finally, future research into the use of performance boundaries for conceptual energy performance design exploration is discussed.

Application of Multidisciplinary Design Optimization in the Casting Process Optimization

Casting process optimization usually includes modifying the mold structure, regulating the pouring temperature, changing pouring time, etc., which involves the mold structure optimization, but also involves optimization of temperature field. In order to get a better process conditions, it will generally make multiple simulation test under the different processes conditions. Many parameters should be set in each simulation test. In order to make casting engineers get liberation from a lot of repetitive work, multidisciplinary design optimization technology is firstly brought into the field of casting, successfully achieved ProCAST integrated into iSIGHT. The method of sample points collected is used in Optimal Latin Hypercube Design, and the building of approximation models is based on Kriging Model. On this basis, the optimization of the objective function is applied to Multi-Island Genetic Algorithm to obtain a global optimal solution. Compared to the verification result of the corresponding simulation, the relative error of the global optimal solution is 0.47%. The result of optimization is ensure average shrinkage rate of casting a smaller value when technological yield of the casting from 54.6% of the existing production case up to 61.1%.

Large-Scale Multidisciplinary Optimization of a Small Satellite’s Design and Operation

The design of satellites and their operation is a complex task that involves a large number of variables and multiple engineering disciplines. Thus, it could benefit from the application of multidisciplinary design optimization, but previous efforts have been hindered by the complexity of the modeling and implementation, discontinuities in the design space, and the wide range of time scales. We address these issues by applying a new mathematical framework for gradient-based multidisciplinary optimization that automatically computes the coupled derivatives of the multidisciplinary system via a generalized form of the adjoint method. The modeled disciplines are orbit dynamics, attitude dynamics, cell illumination, temperature, solar power, energy storage, and communication. Many of these disciplines include functions with discontinuities and nonsmooth regions that are addressed to enable a numerically exact computation of the derivatives for all of the modeled variables. The wide-ranging time scales in the design problem, spanning 30 s to one year, are captured through a combination of multipoint optimization and the use of a small time step in the analyses. Optimizations involving over 25,000 design variables and 2.2 million state variables require 100 h to converge three and five orders of magnitude in optimality and feasibility, respectively. The results show that the geometric design variables yield a 40% improvement in the total data downloaded, which is the objective function, and the operational design variables yield another 40% improvement. This demonstrates not only the value in this approach for the design of satellites and their operation, but also promise for its application to the design of other large-scale engineering systems.

Quantitative Assessment of Multidisciplinary Design Models for Expendable Launch Vehicles

The research effort described in this paper is centered on the development and quantitative assessment of a multidisciplinary design optimization environment for the early design phases of expendable launch vehicles. The focus of the research is on the engineering modeling aspects, with the goal of evaluating in detail the accuracy of engineering-level methods for launch vehicle design, both in terms of disciplinary errors (e.g., engine specific impulse evaluation) and of system-level sensitivities, to assess their applicability to industrial early design. Although aerospace applications of multidisciplinary design optimization can be found in literature, the systematic assessment of the models’ accuracies to the extent described in the present research is a rather new endeavor, which is critical for the advancement of this field. In fact, the widespread industrial application of multidisciplinary design optimization has often been obstructed by the difficulty of finding a suitable compromise between the analysis fidelity and computational cost. Considerable effort was therefore spent on a careful, incremental modeling process, with the purpose of overcoming such an obstacle. As a result, although it is clear that the development and tuning of a reliable multidisciplinary environment is a particularly complex and challenging task, detailed investigations showed that a good compromise can indeed be achieved for the expendable launch vehicles application. In particular, the accuracy on the payload performance was assessed to be in the order of 12% for a computational time per design cycle, allowing one to obtain physically sound design changes through the multidisciplinary design optimization, even exploiting only fast engineering-level methods.

Application of Multidisciplinary Design Optimization to a Resource Satellite

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.

Application of Multidisciplinary Design Optimization Based on Approximation Model to Marine Supercharger Turbo

In this paper, the multidisciplinary design optimization based on Approximation Model for supercharge turbo is studied. Temperature and pressure loads are transferred to the solid model by distance-weighted function, and structure deformation is transferred to aerodynamic model by mesh regenerated method in order to avoid mesh aberration. The Multidisciplinary analysis (MDA) model of supercharge turbo considering aerodynamic, heat transfer, strength and vibration is obtained on the basis of information transferring, which is solved by iterated three times. The Kriging Approximation Model which fits the sample space accurately is employed in the MDO process to reduce computational cost. Results show that performance of supercharge turbo is improvement on the MDO system based on Approximation Model, meanwhile the computational time of the optimization system is saved. Also, this method is suitable for other Multidisciplinary Design Optimization problems.

Performance Modeling and Analysis of a Massively Parallel Direct—Part 1

Modeling and analysis techniques are used to investigate the performance of a massively parallel version of DIRECT, a global search algorithm widely used in multidisciplinary design optimization applications. Several high-dimensional benchmark functions and real world problems are used to test the design effectiveness under various problem structures. Theoretical and experimental results are compared for two parallel clusters with different system scales and network connectivities. The present work aims at studying the performance sensitivity to important parameters for problem configurations, parallel schemes, and system settings. The performance metrics include the memory usage, load balancing, parallel efficiency, and scalability. An analytical bounding model is constructed to measure the load balancing performance under different schemes. Additionally, linear regression models are used to characterize two major overhead sources, interprocessor communication and processor idleness, and also applied to the isoefficiency functions in scalability analysis. For a variety of high-dimensional problems and large-scale systems, the massively parallel design has achieved reasonable performance. The results of the performance study provide guidance for efficient problem and scheme configuration. More importantly, the generalized design considerations and analysis techniques are beneficial for transforming many global search algorithms into effective large-scale parallel optimization tools.

Performance Modeling and Analysis of a Massively Parallel Direct—Part 2

Modeling and analysis techniques are used to investigate the performance of a massively parallel version of DIRECT, a global search algorithm widely used in multidisciplinary design optimization applications. Several high-dimensional benchmark functions and real world problems are used to test the design effectiveness under various problem structures. In this second part of a two-part work, theoretical and experimental results are compared for two parallel clusters with different system scales and network connectivities. The first part studied performance sensitivity to important parameters for problem configurations and parallel schemes, using performance metrics such as memory usage, load balancing, and parallel efficiency. Here linear regression models are used to characterize two major overhead sources, interprocessor communication and processor idleness, and also applied to the isoefficiency functions in scalability analysis. For a variety of high-dimensional problems and large-scale systems, the massively parallel design has achieved reasonable performance. The results of the performance study provide guidance for efficient problem and scheme configuration. More importantly, the design considerations and analysis techniques generalize to the transformation of other global search algorithms into effective large-scale parallel optimization tools.

Stability analysis of underground engineering based on multidisciplinary design optimization

Aiming at characteristics of underground engineering, analyzed the feasibility of Multidisciplinary Design Optimization (MDO) used in underground engineering, and put forward a modularization-based MDO method and the idea of MDO to resolve problems in stability analysis, proving the validity and feasibility of using MDO in underground engineering. Characteristics of uncertainty, complexity and nonlinear become bottle-neck to carry on underground engineering stability analysis by MDO. Therefore, the application of MDO in underground engineering stability analysis is still at a stage of exploration, which need some deep research.

Multi-Attribute Structural Optimization Based on Conjoint Analysis

Over the last 30 years, there have been tremendous advances in multidisciplinary design optimization techniques to find optimum solutions. Advances have come in areas such as reducing computational cost, developing algorithms for efficient sensitivity analysis, using function approximations, etc. Most of these efforts assumed a single objective function (attribute) and a multitude of constraints. The focus of this paper involves the designer preferences in the multidisciplinary design optimization. The concept of modeling preferences among multi-attribute alternatives is prevalent in consumer product marketing. In this paper, we adopt conjoint analysis, a popular technique used in marketing to assess consumer preferences. This technique is used to obtain part-worths of the attributes, which provide insightful knowledge of the product under study, and which are further used to create new products in the market. This paper presents integration of conjoint analysis and multidisciplinary design optimization applications. A cantilever beam, a fixed plate, and a composite lightweight torpedo are used as examples.

Design of an Aircraft Brake Component Using an Interactive Multidisciplinary Design Optimization Framework

A multidisciplinary design optimization (MDO) framework has been used to design an aircraft brake assembly. This was done using a user-interactive implementation of the framework in which design information was obtained from analysis software used in industry but not developed for an MDO application. The design included a number of performance requirements associated with a brake that has been produced for a commercial aircraft. Design improvement was achieved using a practical number of system realizations and the interaction between the optimization algorithm and the design engineers was maintained throughout the process. [S1050-0472(00)00201-4]

Convergence of trust region augmented Lagrangian methods using variable fidelity approximation data

To date the primary focus of most constrained approximate optimization strategies is that application of the method should lead to improved designs. Few researchers have focused on the development of constrained approximate optimization strategies that are assured of converging to a Karush-Kuhn-Tucker (KKT) point for the problem. Recent work by the authors based on a trust region model management strategy has shown promise in managing the convergence of constrained approximate optimization in application to a suite of single level optimization test problems. Using a trust-region model management strategy, coupled with an augmented Lagrangian approach for constrained approximate optimization, the authors have shown in application studies that the approximate optimization process converges to a KKT point for the problem. The approximate optimization strategy sequentially builds a cumulative response surface approximation of the augmented Lagrangian which is then optimized subject to a trust region constraint. In this research the authors develop a formal proof of convergence for the response surface approximation based optimization algorithm. Previous application studies were conducted on single level optimization problems for which response surface approximations were developed using conventional statistical response sampling techniques such as central composite design to query a high fidelity model over the design space. In this research the authors extend the scope of application studies to include the class of multidisciplinary design optimization (MDO) test problems. More importantly the authors show that response surface approximations constructed from variable fidelity data generated during concurrent subspace optimizations (CSSOs) can be effectively managed by the trust region model management strategy. Results for two multidisciplinary test problems are presented in which convergence to a KKT point is observed. The formal proof of convergence and the successfull MDO application of the algorithm using variable fidelity data generated by CSSO are original contributions to the growing body of research in MDO.