scholarly journals Spanning the complexity chasm: A research approach to move from simple to complex engineering systems

2014 ◽  
Vol 28 (4) ◽  
pp. 369-384 ◽  
Author(s):  
Vimal Viswanathan ◽  
Julie Linsey
Keyword(s):  
Design Thinking ◽  
Physical Models ◽  
Research Approach ◽  
Engineering System ◽  
Engineering Systems ◽  
Complex Engineering ◽  
Complex Engineering Systems ◽  
Complex System Design ◽  
Cost Effect

AbstractA multistudy approach is presented that allows design thinking of complex systems to be studied by triangulating causal controlled lab findings with coded data from more complex products. A case study illustration of this approach is provided. During the conceptual design of engineering systems, designers face many cognitive challenges, including design fixation, errors in their mental models, and the sunk cost effect. These factors need to be mitigated for the generation of effective ideas. Understanding the effects of these challenges in a realistic and complex engineering system is especially difficult due to a variety of factors influencing the results. Studying the design of such systems in a controlled environment is extremely challenging because of the scale and complexity of such systems and the time needed to design the systems. Considering these challenges, a mixed-method approach is presented for studying the design thinking of complex engineering systems. This approach includes a controlled experiment with a simple system and a qualitative cognitive-artifacts study on more complex engineering systems followed by the triangulation of results. The triangulated results provide more generalizable information for complex system design thinking. This method combines the advantages of quantitative and qualitative study methods, making them more powerful while studying complex engineering systems. The proposed method is illustrated further using an illustrative study on the cognitive effects of physical models during the design of engineering systems.

scholarly journals Hybrid Data-Driven and Physics-Based Modelling for Gas-Turbine Prescriptive Analytics

2020 ◽  
Author(s):  
Sergei Belov ◽  
Sergei Nikolaev ◽  
Ighor Uzhinsky
Keyword(s):  
Data Driven ◽  
Machine Learning Techniques ◽  
Engineering Systems ◽  
Learning Techniques ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Complex Engineering ◽  
Data Driven Modeling ◽  
Complex Engineering Systems

This paper presents a methodology for predictive and prescriptive analytics of complex engineering systems. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnostics of its flame tube.

Generalized Coupling Management in Complex Engineering Systems Optimization

2011 ◽  
Vol 133 (9) ◽  
Author(s):  
Sulaiman F. Alyaqout ◽  
Diane L. Peters ◽  
Panos Y. Papalambros ◽  
A. Galip Ulsoy
Keyword(s):  
Computational Cost ◽  
System Optimization ◽  
Engineering System ◽  
Engineering Systems ◽  
Solution Strategies ◽  
Weakly Coupled ◽  
System Solution ◽  
Complex Engineering ◽  
Solution Accuracy ◽  
Complex Engineering Systems

Decomposition-based design optimization strategies are used to solve complex engineering system problems that might be otherwise unsolvable. Yet, the associated computational cost can be prohibitively high due to the often large number of iterations needed for coordination of subproblem solutions. To reduce this cost one may exploit the fact that some systems may be weakly coupled and their interactions can be suspended with little loss in solution accuracy. Suspending such interactions is usually based on the analyst’s experience or experimental observation. This article introduces an explicit measure of coupling strength among interconnected subproblems in a decomposed system optimization problem, along with a systematic way for calculating it. The strength measure is then used to suspend weak couplings and, thus, improve system solution strategies such as the model coordination method. Examples show that the resulting strategy may decrease the number of required function evaluations significantly.

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.

A technique to assess the changeability of complex engineering systems

2013 ◽  
Vol 24 (7) ◽  
pp. 477-498 ◽  
Author(s):  
Edwin C.Y. Koh ◽  
Nicholas H.M. Caldwell ◽  
P. John Clarkson
Keyword(s):  
Engineering Systems ◽  
Complex Engineering ◽  
Complex Engineering Systems
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