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.     

An efficient metamodel-based multi-objective multidisciplinary design optimization framework

This paper presents an efficient metamodel-based multi-objective multidisciplinary design optimization (MDO) architecture for solving multi-objective high fidelity MDO problems. One of the important features of the proposed method is the development of an efficient surrogate model-based multi-objective particle swarm optimization (EMOPSO) algorithm, which is integrated with a computationally efficient metamodel-based MDO architecture. The proposed EMOPSO algorithm is based on sorted Pareto front crowding distance, utilizing star topology. In addition, a constraint-handling mechanism in non-domination appointment and fuzzy logic is also introduced to overcome feasibility complexity and rapid identification of optimum design point on the Pareto front. The proposed algorithm is implemented on a metamodel-based collaborative optimization architecture. The proposed method is evaluated and compared with existing multi-objective optimization algorithms such as multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II), using a number of well-known benchmark problems. One of the important results observed is that the proposed EMOPSO algorithm provides high diversity with fast convergence speed as compared to other algorithms. The proposed method is also applied to a multi-objective collaborative optimization of unmanned aerial vehicle wing based on high fidelity models involving structures and aerodynamics disciplines. The results obtained show that the proposed method provides an effective way of solving multi-objective multidisciplinary design optimization problem using high fidelity models.     

Automated generation of multiphysics simulation models to support multidisciplinary design optimization

To ensure a consistent design representation for serving multidisciplinary analysis, this research study proposes an intelligent modeling system to automatically generate multiphysics simulation models to support multidisciplinary design optimization processes by using a knowledge based engineering approach. A key element of this system is a multiphysics information model (MIM), which integrates the design and simulation knowledge from multiple engineering domains. The intelligent modeling system defines classes with attributes to represent various aspects of physical entities. Moreover, it uses functions to capture the non-physical information, such as control architecture, simulation test maneuvers and simulation procedures. The challenge of system coupling and the interactions among the disciplines are taken into account during the process of knowledge acquisition. Depending on the domain requirements, the intelligent modeling system extracts the required knowledge from the MIM and uses this first to instantiate submodels and second to construct the multiphysics simulation model by combining all submodels. The objective of this research is to reduce the time and effort for modeling complex systems and to provide a consistent and concurrent design environment to support multidisciplinary design optimization. The development of an unstable and unmanned aerial vehicle, a multirotor UAV, is selected as test case. The intelligent modeling system is demonstrated by modeling thirty-thousand multirotor UAV designs with different topologies and by ensuring the automatic development of a consistent control system dedicated for each individual design. Moreover, the resulting multiphysics simulation model of the multirotor UAV is validated by comparing with the flight data of an actual quadrotor UAV. The results show that the multiphysics simulation model matches test data well and indicate that high fidelity models can be generated with the automatic model generation process.     

Integrating linear physical programming within collaborative optimization for multiobjective multidisciplinary design optimization

Multidisciplinary design optimization (MDO) is a concurrent engineering design tool for large-scale, complex systems design that can be affected through the optimal design of several smaller functional units or subsystems. Due to the multiobjective nature of most MDO problems, recent work has focused on formulating the MDO problem to resolve tradeoffs between multiple, conflicting objectives. In this paper, we describe the novel integration of linear physical programming within the collaborative optimization framework, which enables designers to formulate multiple system-level objectives in terms of physically meaningful parameters. The proposed formulation extends our previous multiobjective formulation of collaborative optimization, which uses goal programming at the system and subsystem levels to enable multiple objectives to be considered at both levels during optimization. The proposed framework is demonstrated using a racecar design example that consists of two subsystem level analyses — force and aerodynamics — and incorporates two system-level objectives: (1) minimize lap time and (2) maximize normalized weight distribution. The aerodynamics subsystem also seeks to minimize rearwheel downforce as a secondary objective. The racecar design example is presented in detail to provide a benchmark problem for other researchers. It is solved using the proposed formulation and compared against a traditional formulation without collaborative optimization or linear physical programming. The proposed framework capitalizes on the disciplinary organization encountered during large-scale systems design.     

Component-oriented decomposition for multidisciplinary design optimization in building design

The potential of Multidisciplinary Design Optimization (MDO) is not sufficiently exploited in current building design practice. I argue that this field of engineering requires a special setup of the optimization model that considers the uniqueness of buildings, and allows the designer to interact with the optimization in order to assess qualities of aesthetics, expression, and building function. For this reason, the approach applies a performance optimization based on resource consumption extended by preference criteria. Furthermore, building design-specific components serve for the decomposition and an interactive way of working. The component scheme follows the Industry Foundation Classes (IFC) as a common Building Information Model (BIM) standard in order to allow a seamless integration into an interactive CAD working process in the future. A representative case study dealing with a frame-based hall design serves to illustrate these considerations. An N-Square diagram or Design Structure Matrix (DSM) represents the system of components with the disciplinary dependencies and workflow of the analysis. The application of a Multiobjective Genetic Algorithm (MOGA) leads to demonstrable results.     

A solution of multidisciplinary collaborative simulation for complex engineering systems in a distributed heterogeneous environment

This paper presents an integrated approach to multidisciplinary collaborative simulation for complex engineering systems. The formulized paradigm of multidisciplinary collaborative simulation for com- plex engineering systems is principally analyzed. An IEEE HLA and web services based framework is proposed to provide a heterogeneous, distributed and collaborative running environment where multidisciplinary modeling, running management and post-processing of collaborative simulation are undertaken. The mecha...     

Multidisciplinary fault diagnosis of complex engineering systems: A case study of nuclear power plants

Fault diagnosis is a key process in ensuring complex engineering system safety. It often requires collaborative and multidisciplinary efforts. This study seeks to understand the process of multidisciplinary fault diagnosis in complex engineering systems and the key human factors issues that impair this process. Data were collected from multidisciplinary diagnostic activities conducted in the commissioning phase of nuclear power plants (NPPs). In the first phase, we proposed a process model based on a combination of literature review, specialist interviews, and field observations. In phase two, the influencing issues identified in the first phase were assessed through a survey with 117 NPP commissioning specialists. Five factors influencing multidisciplinary fault diagnosis were identified: cognitive artifacts, diagnosis biases, preparation for multidisciplinary diagnosis, information sharing and collaborative reasoning, and collaborative decision-making. The significances of each factor were compared. The results provide guidance for the development of improvement measures to enhance the performance of multidisciplinary fault diagnosis.Practitioner summaryThe processes and influencing issues of multidisciplinary fault diagnosis during the commissioning phase of nuclear power plants were studied with field observations, interviews of 28 specialists, and a survey of 117 specialists. Five major influencing factors were identified, and their influences were compared.     

A web services framework for distributed model management

Distributed model management aims to support the wide-spread sharing and usage of decision support models. Web services is a promising technology for supporting distributed model management activities such as model creation and delivery, model composition, model execution and model maintenance to fulfill dynamic decision-support and problem solving requests. We propose a web services based framework for model management (called MM-WS) to support various activities of the model management life cycle. The framework is based on the recently proposed Integrated Service Planning and Execution (ISP & E) approach for web services integration. We discuss encoding of domain knowledge (as individual models) and utilize the MM-WS framework to interleave synthesis of composite models with their execution. A prototypical implementation with an example is used to illustrate the utility of the framework to enable distributed model management and knowledge integration. Benefits and issues of using the framework to support model-based decision-making in organizational contexts are outlined.

Modified simulated annealing for multiple-objective engineering design optimization

Simulated annealing is adopted as a tool for engineering design optimization. The annealing algorithm was originally designed for single-objective, discrete variable problems. In this study, the annealing algorithm has been significantly modified to handle multiple-objective, continuous variable problems. Cam design is used as a test-bed for the modified annealing algorithm. The result is compared with those from other cam design methods. Computational experience with the modified annealing algorithm is presented and discussed.     

Multidisciplinary interface model for design of mechatronic systems

The design of mechatronic systems is based on the integration of several disciplines, such as mechanical, electrical and software engineering. How to achieve an integrated multidisciplinary design during the development process of mechatronic systems has attracted the attention of both academia and industry. However, solutions which can fully solve this problem have not been proposed by now. The concept of multidisciplinary interface represents the logical or physical relationship integrating the components of the mechatronic system or the components with their environment. As the design of mechatronic systems is a multidisciplinary work, the multidisciplinary interface model can be considered as one of the most effective supports to aid designers for achieving the integrated multidisciplinary design during the development process. The paper presents a multidisciplinary interface model for design of mechatronic systems in order to enable the multidisciplinary integration among design team members from different disciplines. On the one hand, the proposed model ensures the consistency of interface defined by the designers. On the other hand, it helps the designers to guarantee the different components integrate correctly. The interface model including three concepts: classification, data model and compatibility rules. The multidisciplinary interface model is implemented by a case study based on a 3D measurement system.     

Maximum variation analysis based analytical target cascading for multidisciplinary robust design optimization under interval uncertainty

Analytical target cascading (ATC) is a generally used hierarchical method for deterministic multidisciplinary design optimization (MDO). However, uncertainty is almost inevitable in the lifecycle of a complex system. In engineering practical design, the interval information of uncertainty can be more easily obtained compared to probability information. In this paper, a maximum variation analysis based ATC (MVA-ATC) approach is developed. In this approach, all subsystems are autonomously optimized under the interval uncertainty. MVA is used to establish an outer-inner framework which is employed to find the optimal scheme of system and subsystems. All subsystems are coordinated at the system level to search the system robust optimal solution. The accuracy and validation of the presented approach are tested using a classical mathematical example, a heart dipole optimization problem, and a battery thermal management system (BTMS) design problem.     

A multi-objective optimization approach for integrated production planning under interval uncertainties in the steel industry

This paper investigates one of the key decision-making problems referring to the integrated production planning (IPP) for the steelmaking continuous casting-hot rolling (SCC-HR) process in the steel industry. The complexities of the practical IPP problem are mainly reflected in three aspects: large-scale decision variables; multiple objectives and interval-valued uncertain parameters. To deal with the difficulty of large-scale decision variables, we introduce a new concept named “order-set” for modeling. In addition, considering the multiple objectives and uncertainties of the given IPP problem, we construct a multi-objective optimization model with interval-valued objective functions to optimize the throughput of each process, the hot charge ratio of slabs, the utilization rate of tundishes and the additional cost of technical operations. Furthermore, we propose a novel approach based on a modified interval multi-objective optimization evolutionary algorithm (MI-MOEA) to solve the problem. The proposed model and algorithm were tested with daily production data from an iron and steel company in China. Computational experiments demonstrate that the proposed method generates quite effective and practical solutions within a short time. Based on the IPP model and MI-MOEA, an IPP system has been developed and implemented in the company.     

电子商务网站优化综合决策支持系统设计研究

商务网站的质量直接影响到企业在电子市场环境的竞争力,因而如何改进与优化电子商务网站结构成为一个人们十分关注的问题.初步设计了一个电子商务网站优化综合决策支持系统模型(ECWSOSDSS),并提出系统模型应具有的技术特点及主要功能.其目的在于为电子商务网站管理员提供智能化的网站维护的决策支持工具.该系统模型可以为电子商务企业、网站管理员识别网站的优化因素、优化方案的选择及决策提供系统化的技术手段和方法.     

A digital twin-based multidisciplinary collaborative design approach for complex engineering product development

With the ever-increasing demand for personalized product functions, product structure becomes more and more complex. To design a complex engineering product, it involves mechanical, electrical, automation and other relevant fields, which requires a closer multidisciplinary collaborative design (MCD) and integration. However, the traditional design method lacks multidisciplinary coordination, which leads to interaction barriers between design stages and disconnection between product design and prototype manufacturing. To bridge the gap, a novel digital twin-enabled MCD approach is proposed. Firstly, the paper explores how to converge the MCD into the digital design process of complex engineering products in a cyber-physical system manner. The multidisciplinary collaborative design is divided into three parts: multidisciplinary knowledge collaboration, multidisciplinary collaborative modeling and multidisciplinary collaborative simulation, and the realization methods are proposed for each part. To be able to describe the complex product in a virtual environment, a systematic MCD framework based on the digital twin is further constructed. Integrate multidisciplinary collaboration into three stages: conceptual design, detailed design and virtual verification. The ability to verify and revise problems arising from multidisciplinary fusions in real-time minimizes the number of iterations and costs in the design process. Meanwhile, it provides a reference value for complex product design. Finally, a design case of an automatic cutting machine is conducted to reveal the feasibility and effectiveness of the proposed approach.     

基于多级并行策略的复杂产品多学科设计优化

针对复杂产品设计需要进行多学科协作设计和优化的问题,结合计算机应用技术提出基于多级并行策略的多学科优化方法。该方法基于过程建模的层次化优化思想,实现复杂产品设计过程的自动化和优化。减速器标准多学科优化算例说明该方法可实现不同学科的层次化并行优化。将该方法与其他传统的多学科优化方法进行比较,验证该方法的高效性和最优设计的准确性。     

An evolutionary real options framework for the design and management of projects and systems with complex real options and exercising conditions

To address the issue of decision support for designing and managing flexible projects and systems in the face of uncertainties, this paper integrates real options valuation, decision analysis techniques, Monte Carlo simulations and evolutionary algorithms in an evolutionary real options framework. The proposed evolutionary real options framework searches for an optimized portfolio of real options and makes adaptive plans to cope with uncertainties as the future unfolds. Exemplified through a test case, the evolutionary framework not only compares favorably with traditional fixed design approaches but also delivers considerable improvements over prevailing real options practices.     

A knowledge-based master model approach exemplified with jet engine structural design

Successful product development requires the consideration of multiple engineering disciplines and the quantification of tradeoffs among conflicting objectives from the very early design phases. The single-largest challenge to do so is the lack of detailed design information. A possible remedy of this issue is knowledge-based engineering. This paper presents a knowledge-based master model approach that enables the management of concurrent design and analysis models within different engineering disciplines in relation to the same governing product definition. The approach is exemplified on an early phase structural design of a turbo-fan jet engine. The model allows geometric-, structural mechanics- and rotor-dynamic- models to be concurrently integrated into a multi-disciplinary design and optimization loop.     

A new framework of consensus protocol design for complex multi-agent systems

This paper aims to find a simple but efficient method for consensus protocol design. This paper presents two consensus protocols to solve the consensus problem of complex multi-agent systems that consist of inhomogeneous subsystems. The limitations of current studies are analyzed, and a novel model based on transfer functions is presented. This model can be used to describe both homogeneous and inhomogeneous multi-agent systems in a unified framework. Based on this model, two sufficient and necessary conditions for the consensus of complex multi-agent systems have been obtained. One is for the systems without any external input, and the other is for the systems with the same external input. Then, two corresponding distributed consensus protocols are presented. Considering that the complex multi-agent systems may require different outputs sometimes, the relationship between inputs and outputs is analyzed. Finally, some simulations are given to demonstrate the performance and effectiveness of the proposed approaches.