Methodological Principles of Large Scale Complex Engineering Systems Design

1986 ◽  
pp. 237-244
Author(s):  
Tadeusz Basiewicz
Keyword(s):  
Large Scale ◽  
Systems Design ◽  
Engineering Systems ◽  
Complex Engineering ◽  
Methodological Principles ◽  
Complex Engineering Systems

scholarly journals Understanding design change propagation in complex engineering systems using a digital twin and design structure matrix

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Long Chen ◽  
Jennifer Whyte
Keyword(s):  
Large Scale ◽  
Load Capacity ◽  
Prediction Method ◽  
Change Propagation ◽  
Engineering Systems ◽  
Content Type ◽  
Design Change ◽  
Digital Twin ◽  
Complex Engineering ◽  
Complex Engineering Systems

PurposeAs the engineering design process becomes increasingly complex, multidisciplinary teams need to work together, integrating diverse expertise across a range of disciplinary models. Where changes arise, these design teams often find it difficult to handle these design changes due to the complexity and interdependencies inherent in engineering systems. This paper aims to develop an innovative approach to clarifying system interdependencies and predicting the design change propagation at the asset level in complex engineering systems based on the digital-twin-driven design structure matrix (DSM).Design/methodology/approachThe paper first defines the digital-twin-driven DSM in terms of elements and interdependencies, where the authors have defined three types of interdependency, namely, geospatial, physical and logical, at the asset level. The digital twin model was then used to generate the large-scale DSMs of complex engineering systems. The cluster analysis was further conducted based on the improved Idicula–Gutierrez–Thebeau algorithm (IGTA-Plus) to decompose such DSMs into modules for the convenience and efficiency of predicting design change propagation. Finally, a design change propagation prediction method based on the digital-twin-driven DSM has been developed by integrating the change prediction method (CPM), a load-capacity model and fuzzy linguistics. A section of an infrastructure mega-project in London was selected as a case study to illustrate and validate the developed approach.FindingsThe digital-twin-driven DSM has been formally defined by the spatial algebra and Industry Foundation Classes (IFC) schema. Based on the definitions, an innovative approach has been further developed to (1) automatically generate a digital-twin-driven DSM through the use of IFC files, (2) to decompose these large-scale DSMs into modules through the use of IGTA-Plus and (3) predict the design change propagation by integrating a digital-twin-driven DSM, CPM, a load-capacity model and fuzzy linguistics. From the case study, the results showed that the developed approach can help designers to predict and manage design changes quantitatively and conveniently.Originality/valueThis research contributes to a new perspective of the DSM and digital twin for design change management and can be beneficial to assist designers in making reasonable decisions when changing the designs of complex engineering systems.

A Hierarchical Statistical Sensitivity Analysis Method for Complex Engineering Systems Design

2008 ◽  
Vol 130 (7) ◽  
Author(s):  
Xiaolei Yin ◽  
Wei Chen
Keyword(s):  
Sensitivity Analysis ◽  
Systems Design ◽  
Accurate Solution ◽  
General Situation ◽  
Sensitivity Index ◽  
Engineering Systems ◽  
Top Down ◽  
Complex Engineering ◽  
The Impact ◽  
Complex Engineering Systems

Statistical sensitivity analysis (SSA) is playing an increasingly important role in engineering design, especially with the consideration of uncertainty. However, it is not straightforward to apply SSA to the design of complex engineering systems due to both computational and organizational difficulties. In this paper, to facilitate the application of SSA to the design of complex systems especially those that follow hierarchical modeling structures, a hierarchical statistical sensitivity analysis (HSSA) method containing a top-down strategy for SSA and an aggregation approach to evaluating the global statistical sensitivity index (GSSI) is developed. The top-down strategy for HSSA is introduced to invoke the SSA of the critical submodels based on the significance of submodel performances. A simplified formulation of the GSSI is studied to represent the effect of a lower-level submodel input on a higher-level model response by aggregating the submodel SSA results across intermediate levels. A sufficient condition under which the simplified formulation provides an accurate solution is derived. To improve the accuracy of the GSSI formulation for a general situation, a modified formulation is proposed by including an adjustment coefficient (AC) to capture the impact of the nonlinearities of the upper-level models. To improve the efficiency, the same set of samples used in submodel SSAs is used to evaluate the AC. The proposed HSSA method is examined through mathematical examples and a three-level hierarchical model used in vehicle suspension systems design.

A Hierarchical Statistical Sensitivity Analysis Method for Complex Engineering Systems

2007 ◽  
Author(s):  
Xiaolei Yin ◽  
Wei Chen
Keyword(s):  
Sensitivity Analysis ◽  
Hierarchical Modeling ◽  
Systems Design ◽  
Accurate Solution ◽  
General Situation ◽  
Sensitivity Index ◽  
Engineering Systems ◽  
Complex Engineering ◽  
The Impact ◽  
Complex Engineering Systems

The method of Statistical Sensitivity Analysis (SSA) is playing an increasingly important role in engineering design, especially with the consideration of uncertainty. However, applying SSA to the design of complex engineering systems is not straight forward due to both computational and organizational difficulties. In this paper, a Hierarchical Statistical Sensitivity Analysis (HSSA) method is developed to facilitate the application of SSA to the design of complex systems especially those follow hierarchical modeling structures. A top-down strategy for HSSA is introduced to only invoke the SSA of critical submodels based on the significance of submodel performances. A simplified formulation of the Global Statistical Sensitivity Index (GSSI) is studied to represent the effect of a lower-level submodel input on a higher-level model response by aggregating the submodel SSA results across intermediate levels. A sufficient condition under which the simplified formulation provides an accurate solution is derived. To improve the accuracy of the GSSI formulation for a general situation, a modified formulation is proposed by including an Adjustment Coefficient (AC) to capture the impact of the nonlinearities of the upper level models. To save cost, the evaluation of the AC shares the same set of samplings used in the submodel SSA. The proposed HSSA method is examined through mathematical examples and a 3-level hierarchical model used in vehicle suspension systems design.

Effectiveness Of Life Cycle Management Of Projects Using Information Modeling

2019 ◽  
pp. 24-29 ◽  
Keyword(s):  
Life Cycle ◽  
Construction Projects ◽  
Life Cycle Management ◽  
Information Modeling ◽  
Engineering Systems ◽  
Basic Principles ◽  
Complex Engineering ◽  
Domestic Practices ◽  
Capital Construction ◽  
Complex Engineering Systems

Improving the efficiency of life cycle management of capital construction projects using information modeling technologies is one of the important tasks of the construction industry. The paper presents an analysis of accumulated domestic practices, including the legal and regulatory framework, assessing the effectiveness of managing the implementation of investment construction projects and of complex and serial capital construction projects, as well as the life cycle management of especially dangerous technically complex and unique capital construction projects using information modeling technologies, especially capital construction projects, as well as their supporting and using systems, primarily in the nuclear and transport sectors. A review of modern approaches to assessing the effectiveness of life cycle management systems of complex engineering systems in relation to capital construction projects is carried out. The presented material will make it possible to formulate the basic principles and prospects of applying approaches to assessing the effectiveness of the life cycle management system of a capital construction project using information modeling technologies.

Design of Complex Engineering Systems Using Multiagent Coordination

2016 ◽  
Author(s):  
Nicolás F. Soria ◽  
Mitchell K. Colby ◽  
Irem Y. Tumer ◽  
Christopher Hoyle ◽  
Kagan Tumer
Keyword(s):  
Autonomous Agents ◽  
Engineering Systems ◽  
Coordination Problem ◽  
Design Decisions ◽  
Multiagent Coordination ◽  
Trade Offs ◽  
Potential System ◽  
Complex Engineering ◽  
System States ◽  
Complex Engineering Systems

In complex engineering systems, complexity may arise by design, or as a by-product of the system’s operation. In either case, the root cause of complexity is the same: the unpredictable manner in which interactions among components modify system behavior. Traditionally, two different approaches are used to handle such complexity: (i) a centralized design approach where the impacts of all potential system states and behaviors resulting from design decisions must be accurately modeled; and (ii) an approach based on externally legislating design decisions, which avoid such difficulties, but at the cost of expensive external mechanisms to determine trade-offs among competing design decisions. Our approach is a hybrid of the two approaches, providing a method in which decisions can be reconciled without the need for either detailed interaction models or external mechanisms. A key insight of this approach is that complex system design, undertaken with respect to a variety of design objectives, is fundamentally similar to the multiagent coordination problem, where component decisions and their interactions lead to global behavior. The design of a race car is used as the case study. The results of this paper demonstrate that a team of autonomous agents using a cooperative coevolutionary algorithm can effectively design a Formula racing vehicle.

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