Complex System Design Verification Using Assumption Generation

2013 ◽  
Author(s):  
Hoda Mehrpouyan ◽  
Dimitra Giannakopoulou ◽  
Guillaume Brat ◽  
Irem Y. Tumer ◽  
Chris Hoyle
Keyword(s):  
Complex System ◽  
Complex Systems ◽  
System Design ◽  
Discrete Event ◽  
System Level ◽  
Design Verification ◽  
Safety Properties ◽  
Compositional Model ◽  
Counter Example ◽  
Verification Techniques

In the era of large complex systems with continuous and discrete event components, it is critical to establish a complete design verification strategy to determine whether a system satisfies certain safety properties. However, traditional approaches for the verification of such a complex system lack the ability to take into account all possible system states, efficiently model all component interactions, and accurately quantify the risks and uncertainties. This paper presents a methodology for system-level design of complex systems verification based on compositional model checking. This methodology relies on assumption generation and on the domain independent compositional rules for correctness proof of the design of physical systems. The objective is to present a case study for applying the existing automated compositional verification techniques and observing the characteristics of the verification model. The main advantage of this method is that it enables the designer to verify the safety properties of the system without requiring the detail knowledge of the internal actions of the system. The under-approximate context model of the system design is constructed and, in an iterative approach, its safety properties are analyzed until a violation of a property is found and an execution trace called a counter example is produced. In the case of safety requirements violation, the early generation of counter examples leads to faster design verification.

scholarly journals Biased Information Passing Between Subsystems Over Time in Complex System Design

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Jesse Austin-Breneman ◽  
Bo Yang Yu ◽  
Maria C. Yang
Keyword(s):  
Complex System ◽  
System Design ◽  
Large Scale ◽  
Early Stage ◽  
Systems Design ◽  
System Level ◽  
Worst Case ◽  
Early Stage Design ◽  
Complex System Design ◽  
Biased Information

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.

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

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.

Toward a Functional Failure Modeling Method of Representing Prognostic Systems During the Early Phases of Design

2015 ◽  
Author(s):  
Caitlin Stack ◽  
Douglas L. Van Bossuyt
Keyword(s):  
Complex System ◽  
Complex Systems ◽  
System Design ◽  
Power Plants ◽  
Pressurized Water ◽  
Functional Failure ◽  
Early Stages ◽  
Failure Modeling ◽  
Complex System Design ◽  
Early Phases

Current methods of functional failure risk analysis do not facilitate explicit modeling of systems equipped with Prognostics and Health Management (PHM) hardware. As PHM systems continue to grow in application and popularity within major complex systems industries (e.g. aerospace, automotive, civilian nuclear power plants), implementation of PHM modeling within the functional failure modeling methodologies will become useful for the early phases of complex system design and for analysis of existing complex systems. Functional failure modeling methods have been developed in recent years to assess risk in the early phases of complex system design. However, the methods of functional modeling have yet to include an explicit method for analyzing the effects of PHM systems on system failure probabilities. It is common practice within the systems health monitoring industry to design the PHM subsystems during the later stages of system design — typically after most major system architecture decisions have been made. This practice lends itself to the omission of considering PHM effects on the system during the early stages of design. This paper proposes a new method for analyzing PHM subsystems’ contribution to risk reduction in the early stages of complex system design. The Prognostic Systems Variable Configuration Comparison (PSVCC) eight-step method developed here expands upon existing methods of functional failure modeling by explicitly representing PHM subsystems. A generic pressurized water nuclear reactor primary coolant loop system is presented as a case study to illustrate the proposed method. The success of the proposed method promises more accurate modeling of complex systems equipped with PHM subsystems in the early phases of design.

scholarly journals Biased Information Passing Between Subsystems Over Time in Complex System Design

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.

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

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.

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

2011 ◽  
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.

Toward a Dedicated Failure Flow Arrestor Function Methodology

2015 ◽  
Author(s):  
Michael R. S. Slater ◽  
Douglas L. Van Bossuyt
Keyword(s):  
Complex System ◽  
Complex Systems ◽  
System Design ◽  
Nuclear Power ◽  
Nuclear Regulatory Commission ◽  
Flow State ◽  
Pressurized Water ◽  
Functional Failure ◽  
Failure Modeling ◽  
Complex System Design

Risk analysis in engineering design is of paramount importance when developing complex systems or upgrading existing systems. In many complex systems, new generations of systems are expected to have decreased risk and increased reliability when compared with previous designs. For instance, within the American civilian nuclear power industry, the Nuclear Regulatory Commission (NRC) has progressively increased requirements for reliability and driven down the chance of radiological release beyond the plant site boundary. However, many ongoing complex system design efforts analyze risk after early major architecture decisions have been made. One promising method of bringing risk considerations earlier into the conceptual stages of the complex system design process is functional failure modeling. Function Failure Identification and Propagation (FFIP) and related methods began the push toward assessing risk using the functional modeling taxonomy. This paper advances the Dedicated Failure Flow Arrestor Function (DFFAF) method which incorporates dedicated Arrestor Functions (AFs) whose purpose is to stop failure flows from propagating along uncoupled failure flow pathways, as defined by Uncoupled Failure Flow State Reasoner (UFFSR). By doing this, DFFAF provides a new tool to the functional failure modeling toolbox for complex system engineers. This paper introduces DFFAF and provides an illustrative simplified civilian Pressurized Water Reactor (PWR) nuclear power plant case study.

Target Management in Complex System Design Using System Norms

2004 ◽  
Vol 127 (4) ◽  
pp. 536-544 ◽  
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.

scholarly journals Description of approaches to the study of decentralized complex systems in the management of the regional construction cluster

2018 ◽  
Vol 251 ◽  
pp. 05008
Author(s):  
Dmitrii Anufriev ◽  
Artem Holodov
Keyword(s):  
Complex System ◽  
Complex Systems ◽  
System Dynamics ◽  
Simulation Model ◽  
Economic System ◽  
Discrete Event ◽  
Queuing System ◽  
Queuing Network ◽  
Centralized Management ◽  
Probabilistic Structure

Regional construction cluster, which is considered as a decentralized socio-economic system, territorially localized within the region, consisting of interconnected enterprises of construction and related industries. Development of a method for centralized management of a complex socio-economic system based on the integration of simulation approaches. Approaches are considered that allow the transition from consideration of a decentralized complex system to the management of a centralized structure through the introduction of image agents and the formation of a multiphase queuing network, where the four-channel queuing system with expectations is considered as the phase. The structure of the simulation model is presented, which makes it possible to model the functionality of the regional construction cluster. The approaches to integration of the agent approach with modules of system dynamics and modules implementing the discrete-event approach for simulation of business pro-cesses in a complex socio-economic system that take into account the probabilistic structure of the emerging processes are considered in the article.

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