Multidisciplinary Design Optimization Strategy Research Articles

A novel Kriging-model-assisted reliability-based multidisciplinary design optimization strategy and its application in the offshore wind turbine tower

In Reliability-based Multidisciplinary Design Optimization (RBMDO), the key performance functions of wind turbine are usually implicit, which means the performance response can only be obtained through time-consuming Physics Experiment (PE) or Finite Element Analysis (FEA). However, for practical engineering, the computational cost of repeatedly using PE or FEA is prohibitive. To tackle this challenge, in this study, an adaptive Kriging-model-assisted RBMDO strategy is proposed. The novel updated-strategy for performance function in RBMDO is discussed to find effective training samples of active learning for Kriging model. Also, a powerful decoupling strategy of RBMDO is introduced and combined with the proposed method to enhance computational efficiency further. Two case studies, including a mathematic example and a hydraulic turbine rotor bracket design example, are utilized to illustrate the advantage of the given strategy. Finally, the proposed method is applicated into an engineering design of 5 MW offshore wind turbine tower to ensure its reliability and safety.

A Reliability-Based Multidisciplinary Design Optimization Strategy considering Interval Uncertainty Based on the Point-Infilled Kriging Model

Generally, traditional uncertainty design optimization (UDO) methods are based on probability density distribution function or fuzzy membership function. In this situation, a large amount of uncertain information is necessary to construct the UDO model accurately. While, the interval UDO methods require less design information. Only the upper and lower bounds of interval uncertainty are utilized to construct the optimization model. In this study, to enhance the efficiency and accuracy of UBDO considering interval uncertainty, a reliability-based multidisciplinary design optimization (RBMDO) strategy using the point-infilled Kriging model is proposed. In the given method, a double-nested RBMDO model considering interval uncertainty is established. The collaborative optimization is utilized to deal with coupling relationships among complex systems. Then, the point-infilled Kriging response surface strategy is introduced to approximate the RBMDO model. The procedure of the interval multidisciplinary collaborative optimization method based on the Kriging model is discussed. Two examples are given to illustrate the application of the proposed method.

Multidisciplinary design optimization of a wide speed range vehicle with waveride airframe and RBCC engine

The wide speed range vehicle is believed to be ideal for space round trip and its design process incorporates a number of disciplines. To achieve an overall optimal design, we adopt the multidisciplinary design optimization (MDO) approach to design the wide speed range vehicle with waverider being the airframe and the rocket-based combine cycle (RBCC) engine being the propulsion system. The design process includes the modeling of each discipline, the MDO system integration and the operation of the MDO system. Disciplines integrated in the MDO architecture includes the geometry, the aerodynamics, the propulsion, the structural mass and the trajectory. The modeling processes of these disciplines are introduced in brief in this paper and some of them are novel in the relevant area. The Bi-level system integrated optimization (BLSIO) approach is proposed as the MDO strategy, which optimize the trajectory discipline using the state variables of the other disciplines in each iteration process. With an aircraft climb mission being the optimize background, the MDO process is applied successfully and the optimal design obtained by the MDO process reduced the minimum climb time by 10.02% in comparison with the reference model. The analysis shows that the reduce of the minimum climb time is mainly realized by optimizing the configuration of the vehicle and adapting the attack angle in the climb process. The optimal configuration has larger lift-to-drag ratio but fewer fuel remaining after the mission than the reference model. This concludes that although the optimized model is not superior to the reference model in all aspects, it has an overall performance within the design space. Also, the BLSIO approach is ideal in solving the MDO problem of the wide speed range mission.

MDO strategy for meridian plane design to improve energy conversion capability of LFR main coolant pump

The design of the LFR main coolant pump (MCP) is a balanced solution between multiple disciplines. Based on the multidisciplinary design optimization (MDO) software ISIGHT, the pump design software CFturbo, PumpLinx and MATLAB are systematically integrated. The geometric structure of the pump is parameterized, in which 6 meridian plane parameters are selected as input parameters, efficiency, head and shaft power are selected as output parameters. Based on the optimal Latin hypercube design, the six input parameters are spatially arranged, and then the RSM models between the input and output are established respectively according to the calculation results. Finally, the six input parameters and the three response indexes are optimized based on the multi-objective optimization algorithm. The method effectively balances the complex design requirements of the MCP, and finally obtains the influence relationship between the coupling effects of different geometric parameters of the meridian plane on the hydraulic performance.

Multidisciplinary Design Optimization of a Novel Sandwich Beam-Based Adaptive Tuned Vibration Absorber Featuring Magnetorheological Elastomer.

The present study aims to investigate the dynamic performance and design optimization of a novel magnetorheological elastomer based adaptive tuned vibration absorber (MRE-ATVA). The proposed MRE-ATVA consists of a light-weight sandwich beam treated with an MRE core layer and two electromagnets installed at both free ends. Three different design configurations for electromagnets are proposed. The finite element (FE) model of the proposed MRE-ATVA and magnetic model of the electromagnets are developed and combined to evaluate the frequency range of the absorber under varying magnetic field intensity. The results of the developed model are validated in multiple stages with available analytical and simulation data. A multidisciplinary design optimization strategy has been formulated to maximize the frequency range of the proposed MRE-based ATVA while respecting constraints of weight, size, mechanical stress, and sandwich beam deflection. The optimal solution is obtained and compared for the three proposed ATVA configurations. The optimal ATVA with a U-shaped electromagnet shows more than 40% increase in the natural frequency while having a total mass of 596 g.

Design of lightweight magnesium car body structure under crash and vibration constraints

Car body design in view of structural performance and lightweighting is a challenging task due to all the performance targets that must be satisfied such as vehicle safety and ride quality. In this paper, material replacement along with multidisciplinary design optimization strategy is proposed to develop a lightweight car body structure that satisfies the crash and vibration criteria while minimizing weight. Through finite element simulations, full frontal, offset frontal, and side crashes of a full car model are evaluated for peak acceleration, intrusion distance, and the internal energy absorbed by the structural parts. In addition, the first three fundamental natural frequencies are combined with the crash metrics to form the design constraints. The wall thicknesses of twenty-two parts are considered as the design variables. Latin Hypercube Sampling is used to sample the design space, while Radial Basis Function methodology is used to develop surrogate models for the selected crash responses at multiple sites as well as the first three fundamental natural frequencies. A nonlinear surrogate-based optimization problem is formulated for mass minimization under crash and vibration constraints. Using Sequential Quadratic Programming, the design optimization problem is solved with the results verified by finite element simulations. The performance of the optimum design with magnesium parts shows significant weight reduction and better performance compared to the baseline design.

A web services-based multidisciplinary design optimization framework for complex engineering systems with uncertainties

Multidisciplinary design optimization (MDO) involves multiple disciplines, multiple coupled relationships and multiple processes, which is implemented by different specialists dispersed geographically on heterogeneous platforms with different analysis and optimization tools. The product design data integration and data sharing among the participants hampers the development and applications of MDO in enterprises seriously. Therefore, a multi-hierarchical integrated product design data model (MH-iPDM) supporting the MDO in web environment and a web services-based MDO framework considering uncertainties are proposed in this paper. Based on the enabling technologies including web services, ontology, workflow, agent, XML, and evidence theory, the proposed framework enables the designers geographical dispersed to work collaboratively in the MDO environment. The ontology-based workflow enables the logical reasoning of MDO to be processed dynamically. The evidence theory-based uncertainty reasoning and analysis supports the quantification, aggregation and analysis of the conflicting epistemic uncertainty from multiple sources, which improves the reliability of product. Finally, a proof-of-concept prototype system is developed based on Java 2 Platform Enterprise Edition (J2EE) and an example of supersonic business jet is demonstrated to verify the autonomous execution of MDO strategies and the effectiveness of the proposed approach.

Optimal Design of Hybrid Electric Fuel Cell Vehicles Under Uncertainty and Enterprise Considerations

Hybrid electric fuel cell vehicles are studied using a system perspective that explores the tradeoffs among fuel economy, acceleration, and other vehicle attributes. Design decisions are derived from an enterprise decision-making model that seeks to maximize profit. Uncertainties stemming from manufacturing variability, customer preferences, and market response to fuel price fluctuations are also included. The system is hierarchically partitioned into three levels that comprise enterprise, powertrain, fuel cell, and battery subsystem models. Analytical target cascading, a decomposition-based multidisciplinary design optimization strategy, is used to solve the resulting problem. Results indicate a strong interaction between enterprise and engineering considerations, and a significant impact of uncertainty on the optimal system design.

Sequential optimization and reliability assessment for multidisciplinary systems design

With higher reliability and safety requirements, reliability-based design has been increasingly applied in multidisciplinary design optimization (MDO). A direct integration of reliability-based design and MDO may present tremendous implementation and numerical difficulties. In this work, a methodology of sequential optimization and reliability assessment for MDO is proposed to improve the efficiency of reliability-based MDO. The central idea is to decouple the reliability analysis from MDO with sequential cycles of reliability analysis and deterministic MDO. The reliability analysis is based on the first-order reliability method (FORM). In the proposed method, the reliability analysis and the deterministic MDO use two MDO strategies, the multidisciplinary feasible approach and the individual disciplinary feasible approach. The effectiveness of the proposed method is illustrated with two example problems.

Trust region model management in multidisciplinary design optimization

A common engineering practice is the use of approximation models in place of expensive computer simulations to drive a multidisciplinary design process based on nonlinear programming techniques. The use of approximation strategies is designed to reduce the number of detailed, costly computer simulations required during optimization while maintaining the pertinent features of the design problem. This paper overviews the current state of the art in model management strategies for approximate optimization. Model management strategies coordinate the interaction between the optimization and the fidelity of the approximation models so as to ensure that the process converges to a solution of the original design problem. Approximations play an important role in multidisciplinary design optimization (MDO) by offering system behavior information at a relatively low cost. Most approximate MDO strategies are sequential, in which an optimization of an approximate problem subject to design variable move limits is iteratively repeated until convergence. The move limits or trust region are imposed to restrict the optimization to regions of the design space in which the approximations provide meaningful information. In order to insure convergence of the sequence of approximate optimizations to a Karush–Kuhn–Tucker solution, a trust region model management or move limit strategy is required. In this paper recent developments in approximate MDO strategies and issues of trust region model management in MDO are reviewed.

Aeroelastic Tailoring of Composite Structures

A wing-design optimization study is conducted on a composite wing. The objective is to evaluate the effect of the compositelayup orientation on theoptimized weight while satisfying constraints on strength, roll-reversalvelocity, and e utter velocity. The wing optimization studies are presented with the composite layups oriented at 5-deg increments up to § 20 deg from the midspar of the wing. The multidisciplinary optimization system, ASTROS, was used in the design study. This study, although not conclusive, indicates that optimal designs when subjected to multiple structural constraints are relatively insensitive to the orientation of the laminate layup. I. Introduction M ULTIDISCIPLINARYdesign optimization (MDO)hasbeen the subject of numerous investigations in recent years. This technology push is primarily attributed to the rapid growth in speed, data transfer,and storage capabilities of modern computers. Itis anticipated that in a few years the speed of computation may surpass the Ge ops/s range with similar improvements in memory devices. The computer’ s capabilities are doubling or tripling in a relatively short time, while the cost of computation and information processing is reducing by similar factors. These far-reaching developments in computers are the propelling force behind the rapid evolution of the so-called information highway . The information explosion is already affecting almost every facet of life in industrialized countrieswhilepushing industrializationofthe remainingcountries.The world is a very competitive market place for products. It is expected to be even better for the consumer. Cost, quality, and innovation are the key ingredients for success in such an environment. A wellorchestrated MDO strategy is the best means for improvement in all three areas. It is often the mistakes made in early design that haunt the bad performance of the products. MDO systems on modern computers allow the rapid evaluation of the operation scenarios of the products. The rapid strides in computers are the catalyst in promoting sophisticated computer architectures, database technology, and numerical methods. These developments, in turn, are providing the necessary infrastructure for integration of both disciplines and the designstages.Forexample,inairframedevelopment,aerodynamics, structures, and controls are the main disciplines; whereas in design, manufacture, operation, and maintenance are the stages of development. Each of these stages is further divided into manageable tasks that are appropriate to available resources. For example, the design task is traditionally conducted in conceptual, preliminary, and detail design stages. If we couple this view of design with the task of integration of the disciplines (such as aerodynamics, structures, and controls ), we have an almost intractable optimization problem, where the overall minimum cost is the primary driver. It is under