Biased Information Passing Between Subsystems Over Time in Complex System Design

During the early stage design of large-scale engineering systems, design teams are challenged to balance a complex set of considerations. The established structured approaches for optimizing complex system designs offer strategies for achieving optimal solutions, but in practice suboptimal system-level results are often reached due to factors such as satisficing, ill-defined problems, or other project constraints. Twelve subsystem and system-level practitioners at a large aerospace organization were interviewed to understand the ways in which they integrate subsystems in their own work. Responses showed subsystem team members often presented conservative, worst-case scenarios to other subsystems when negotiating a tradeoff as a way of hedging against their own future needs. This practice of biased information passing, referred to informally by the practitioners as adding “margins,” is modeled in this paper with a series of optimization simulations. Three “bias” conditions were tested: no bias, a constant bias, and a bias which decreases with time. Results from the simulations show that biased information passing negatively affects both the number of iterations needed and the Pareto optimality of system-level solutions. Results are also compared to the interview responses and highlight several themes with respect to complex system design practice.

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Biased Information Passing Between Subsystems Over Time in Complex System Design

Volume 7: 2nd Biennial International Conference on Dynamics for Design; 26th International Conference on Design Theory and Methodology ◽

10.1115/detc2014-34433 ◽

2014 ◽

Author(s):

Jesse Austin-Breneman ◽

Bo Yang Yu ◽

Maria C. Yang

Keyword(s):

Complex System ◽

System Design ◽

Large Scale ◽

Early Stage ◽

System Level ◽

Worst Case ◽

Team Members ◽

Early Stage Design ◽

Complex System Design ◽

Biased Information

The early stage design of large-scale engineering systems challenges design teams to balance a complex set of considerations. Established structured approaches for optimizing complex system designs offer strategies for achieving optimal solutions, but in practice sub-optimal system-level results are often reached due to factors such as satisficing, ill-defined problems or other project constraints. Twelve sub-system and system-level practitioners at a large aerospace organization were interviewed to understand the ways in which they integrate sub-systems. Responses showed sub-system team members often presented conservative, worst-case scenarios to other sub-systems when negotiating a trade-off as a way of hedging their own future needs. This practice of biased information passing, referred to informally by the practitioners as adding “margins,” is modeled with a series of optimization simulations. Three “bias” conditions were tested: no bias, a constant bias and a bias which decreases with time. Results from the simulations show that biased information passing negatively affects both the number of iterations needed to reach and the Pareto optimality of system-level solutions. Results are also compared to the interview responses and highlight several themes with respect to complex system design practice.

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Changing Subsystem Information Strategies Using Weighted Objectives: Increasing Robustness to Biased Information Passing

Volume 2A: 41st Design Automation Conference ◽

10.1115/detc2015-47667 ◽

2015 ◽

Author(s):

Jesse Austin-Breneman ◽

Bo Yang Yu ◽

Maria C. Yang

Keyword(s):

Complex System ◽

System Design ◽

Field Research ◽

Local Optimization ◽

System Level ◽

Incentive Structure ◽

Information Strategies ◽

System Robustness ◽

Complex System Design ◽

Biased Information

Complex system design requires managing competing objectives between many subsystems. Previous field research has demonstrated that subsystem designers may use biased information passing as a negotiation tactic and thereby reach sub-optimal system-level results due to local optimization behavior. One strategy to combat the focus on local optimization is an incentive structure that promotes system-level optimization. This paper presents a new subsystem incentive structure based on Multi-disciplinary Optimization (MDO) techniques for improving robustness of the design process to such biased information passing strategies. Results from simulations of different utility functions for a test suite of multi-objective problems quantify the system robustness to biased information passing strategies. Results show that incentivizing subsystems with this new weighted structure may decrease the error resulting from biased information passing.

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A Study of Student Design Team Behaviors in Complex System Design

Journal of Mechanical Design ◽

10.1115/1.4007840 ◽

2012 ◽

Vol 134 (12) ◽

Cited By ~ 18

Author(s):

Jesse Austin-Breneman ◽

Tomonori Honda ◽

Maria C. Yang

Keyword(s):

Complex System ◽

System Design ◽

System Integration ◽

Large Scale ◽

Engineering Students ◽

System Level ◽

Design Parameters ◽

Local Searches ◽

Wide Range ◽

Complex System Design

Large-scale engineering systems require design teams to balance complex sets of considerations using a wide range of design and decision-making skills. Formal, computational approaches for optimizing complex systems offer strategies for arriving at optimal solutions in situations where system integration and design optimization are well-formulated. However, observation of design practice suggests engineers may be poorly prepared for this type of design. Four graduate student teams completed a distributed, complex system design task. Analysis of the teams' design histories suggests three categories of suboptimal approaches: global rather than local searches, optimizing individual design parameters separately, and sequential rather than concurrent optimization strategies. Teams focused strongly on individual subsystems rather than system-level optimization, and did not use the provided system gradient indicator to understand how changes in individual subsystems impacted the overall system. This suggests the need for curriculum to teach engineering students how to appropriately integrate systems as a whole.

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Understanding the Impact of Decision Making on Robustness During Complex System Design: More Resilient Power Systems

ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg ◽

10.1115/1.4044471 ◽

2020 ◽

Vol 6 (2) ◽

Author(s):

Joseph R. Piacenza ◽

Kenneth John Faller ◽

Mir Abbas Bozorgirad ◽

Eduardo Cotilla-Sanchez ◽

Christopher Hoyle ◽

...

Keyword(s):

Complex System ◽

Decision Making ◽

System Design ◽

Robust Design ◽

System Performance ◽

System Level ◽

Individual Risk ◽

Cascading Failure ◽

The Impact ◽

Complex System Design

Abstract Robust design strategies continue to be relevant during concept-stage complex system design to minimize the impact of uncertainty in system performance due to uncontrollable external failure events. Historical system failures such as the 2003 North American blackout and the 2011 Arizona-Southern California Outages show that decision making, during a cascading failure, can significantly contribute to a failure's magnitude. In this paper, a scalable, model-based design approach is presented to optimize the quantity and location of decision-making agents in a complex system, to minimize performance loss variability after a cascading failure, regardless of where the fault originated in the system. The result is a computational model that enables designers to explore concept-stage design tradeoffs based on individual risk attitudes (RA) for system performance and performance variability, after a failure. The IEEE RTS-96 power system test case is used to evaluate this method, and the results reveal key topological locations vulnerable to cascading failures, that should not be associated with critical operations. This work illustrates the importance of considering decision making when evaluating system level tradeoffs, supporting robust design.

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Capturing Interactions and Emergent Failure Behavior in Complex Engineered Systems at Multiple Scales

Volume 2: 31st Computers and Information in Engineering Conference, Parts A and B ◽

10.1115/detc2011-47767 ◽

2011 ◽

Cited By ~ 8

Author(s):

Nikolaos Papakonstantinou ◽

Seppo Sierla ◽

David C. Jensen ◽

Irem Y. Tumer

Keyword(s):

Complex System ◽

System Design ◽

Emergent Behavior ◽

System Level ◽

Design Stage ◽

Failure Behavior ◽

Early Design ◽

Large Complex ◽

Large Complex System ◽

Complex System Design

Large complex systems exhibit complex nominal and failure behavior and understanding that behavior is critical to the accurate assessment of risk. However, this assessment is difficult to accomplish in the early design stage. Multiple subsystem interactions and emergent behavior further complicate early design risk analysis. The goal of this paper is to demonstrate necessary modifications of an existing function-based failure assessment tool for application to the large complex system design domain. Specifically, this paper demonstrates how specific adaptations to this early, qualitative approach to system behavioral simulation and analysis help overcome some of the challenges to large complex system design. In this paper, a boiling water nuclear reactor design serves as a motivating case study for showing how this approach can capture complex subsystem interactions, identify emergent behavior trends, and assess failures at both the component and system level.

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Target Management in Complex System Design Using System Norms

Journal of Mechanical Design ◽

10.1115/1.1897404 ◽

2004 ◽

Vol 127 (4) ◽

pp. 536-544 ◽

Cited By ~ 5

Author(s):

H. Mahmoud ◽

P. Kabamba ◽

A. G. Ulsoy ◽

G. Brusher

Keyword(s):

Complex System ◽

System Design ◽

Output Signal ◽

System Performance ◽

Mimo Systems ◽

System Level ◽

Engineering System ◽

Subsystem Design ◽

Target Management ◽

Complex System Design

The problem of setting, balancing, and determining priorities of design targets among the subsystems constituting an engineering system, i.e., managing the targets, is addressed. A new norm-based benchmarking approach is proposed to relate the system-level design objectives to subsystem design targets. The proposed approach provides a systematic means of setting and balancing subsystem design targets to deliver the desired system performance and ranks the priorities of the subsystem targets. Furthermore, the use of system norms, rather than output signal norms, to quantify system and subsystem performance reduces the number of design targets in multi-input multi-output (MIMO) systems. The approach is illustrated on a vehicle example, consisting of a frame, body, and body mounts as the subsystems.

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Accurate Complex Systems Design: Integrating Serious Games with Petri Nets

International Journal of Serious Games ◽

10.17083/ijsg.v3i1.105 ◽

2016 ◽

Vol 3 (1) ◽

Author(s):

Kirsten Sinclair ◽

Daniel Livingstone

Keyword(s):

Complex System ◽

Complex Systems ◽

Petri Nets ◽

System Design ◽

Serious Games ◽

Systems Design ◽

Serious Game ◽

Interactive Simulation ◽

Graphical Modelling ◽

Complex System Design

Difficulty understanding the large number of interactions involved in complex systems makes their successful engineering a problem. Petri Nets are one graphical modelling technique used to describe and check proposed designs of complex systems thoroughly. While automatic analysis capabilities of Petri Nets are useful, their visual form is less so, particularly for communicating the design they represent. In engineering projects, this can lead to a gap in communications between people with different areas of expertise, negatively impacting achieving accurate designs.In contrast, although capable of representing a variety of real and imaginary objects effectively, behaviour of serious games can only be analysed manually through interactive simulation. This paper examines combining the complementary strengths of Petri Nets and serious games. The novel contribution of this work is a serious game prototype of a complex system design that has been checked thoroughly. Underpinned by Petri Net analysis, the serious game can be used as a high-level interface to communicate and refine the design.Improvement of a complex system design is demonstrated by applying the integration to a proof-of-concept case study.

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Selecting a Compressor Meridional Topology: Axial, Mixed, Radial

10.1115/gt2021-59121 ◽

2021 ◽

Author(s):

Jonathan M. Smyth ◽

Robert J. Miller

Keyword(s):

Design Optimization ◽

Early Stage ◽

New Products ◽

Systems Design ◽

Blade Design ◽

Improved Method ◽

Stage Design ◽

Specific Speed ◽

Early Stage Design ◽

Smith Chart

Abstract This paper proposes a new duty-based Smith Chart as part of an improved method of selecting the geometric topology of compressors (axial, mixed or radial) in the earliest stage of design. The method has a number of advantages over previous methods: it is based on the non-dimensional flow and the non-dimensional work, which aligns with the aerodynamic function of the compressor and is therefore more intuitive than specific speed and specific diameter. It is based on a large number of consistently designed compressor rotors which have been computationally predicted using RANS CFD. Most importantly, it provides the designer not only with a choice of topology but also with the complete meridional geometry of the compressor, its blade design and the number of blades. This fidelity of geometry at the very early stage of design allows the designer to undertake a true systems design optimization (noise, manufacturing, packaging constraints and cost). This has the major advantage of significantly reducing early stage design times and costs and allows the designer to explore completely new products more quickly.

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