Isoperformance: Analysis and design of complex systems with desired outcomes

Weck, Olivier L. de; Jones, Marshall B.

doi:10.1002/sys.20043

Cited by 34 publications

(10 citation statements)

References 8 publications

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“…For instance, as shown in figure 3b, higher thermal efficiency at the building level can be equally achieved by increasing the thermal resistance of the envelope or by decreasing the air infiltration rate. That is, all weatherproofing solutions are located on an iso-performance line [39] in the (R env , I env ) space at a fixed h H , where any point corresponds to a unique value of R eff . This finding was further tested via standard machine learning methods, namely by means of multiple adaptive regression splines (MARS) [40,41], which are well suited for capturing response surfaces of multiparametric problems [42,43].…”

Section: Resultsmentioning

confidence: 99%

Data analytics for simplifying thermal efficiency planning in cities

Qomi

Noshadravan

Sobstyl³

et al. 2016

J. R. Soc. Interface.

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More than 44% of building energy consumption in the USA is used for space heating and cooling, and this accounts for 20% of national CO 2 emissions. This prompts the need to identify among the 130 million households in the USA those with the greatest energy-saving potential and the associated costs of the path to reach that goal. Whereas current solutions address this problem by analysing each building in detail, we herein reduce the dimensionality of the problem by simplifying the calculations of energy losses in buildings. We present a novel inference method that can be used via a ranking algorithm that allows us to estimate the potential energy saving for heating purposes. To that end, we only need consumption from records of gas bills integrated with a building's footprint. The method entails a statistical screening of the intricate interplay between weather, infrastructural and residents' choice variables to determine building gas consumption and potential savings at a city scale. We derive a general statistical pattern of consumption in an urban settlement, reducing it to a set of the most influential buildings' parameters that operate locally. By way of example, the implications are explored using records of a set of (N ¼ 6200) buildings in Cambridge, MA, USA, which indicate that retrofitting only 16% of buildings entails a 40% reduction in gas consumption of the whole building stock. We find that the inferred heat loss rate of buildings exhibits a power-law data distribution akin to Zipf's law, which provides a means to map an optimum path for gas savings per retrofit at a city scale. These findings have implications for improving the thermal efficiency of cities' building stock, as outlined by current policy efforts seeking to reduce home heating and cooling energy consumption and lower associated greenhouse gas emissions.

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Section: Resultsmentioning

confidence: 99%

Data analytics for simplifying thermal efficiency planning in cities

Qomi

Noshadravan

Sobstyl³

et al. 2016

J. R. Soc. Interface.

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“…The number of iterations was averaged over 50 random starting points. The Euclidean distance to the Pareto Frontier was normalized by the Euclidean distance between the Pareto maximum and minimum [1]. A value of zero would indicate a solution directly on the Pareto Frontier and a value of 100% would indicate a solution at the normalizing distance.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…However, in practice sub-optimal results are often reached at the system level. This can be due to many factors: satisficing decisionmaking [1], time or budget constraints or ill-defined problems [2].…”

Section: Introductionmentioning

confidence: 99%

Biased Information Passing Between Subsystems Over Time in Complex System Design

Yang

Austin‐Breneman

2014

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

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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 systemlevel 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 * Corresponding author.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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“…Ilities are quality attributes that emerge when engineering systems are integrated and operated, for which approaches are available in different domains [5][6][7] with a variety of definitions [8][9][10][11][12]. We note that when a system is in operation, new properties emerge due to interactions between parts of the system.…”

Section: Characterization and Scope Of Ilities In Systems Engineeringmentioning

confidence: 99%

On Using Ilities of Non-Functional Properties for Subsystems and Components

Lee

Collins

2017

Systems

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Abstract:The use of ilities for systems engineering of subsystems and components is investigated. Prior work on ilities has emphasized or restricted their application to system level, non-functional properties. The premise of this work is that ilities can be applied with benefit, and in some cases out of necessity, to lower levels of systems as well. The veracity of this premise is established by providing examples that demonstrate how some ilities are passed and used as a non-functional property of electrical and structural subsystems in aircraft. It is further demonstrated that flowing ilities down to the subsystem level is not only a useful practice for systems engineers, it can also be an essential step to ensure that customer needs are actually met by the system under design or service. Systems engineers often lack the detailed knowledge of the subsystems or components required to translate ilities into functional requirements. Thus, the system ilities are passed down and translated from non-functional to functional requirements by subject matter experts. We first discuss the definition, characteristics and scope of ilities. Then, we formulate the application of ilities at a subsystem level. Next, we show aircraft engineering examples for ilities applications. The application process is formalized with diagrams, and ilities' relation to system architecture engineering is discussed. The work concludes with a summary and suggestions for future work.

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Isoperformance: Analysis and design of complex systems with desired outcomes

Cited by 34 publications

References 8 publications

Data analytics for simplifying thermal efficiency planning in cities

Data analytics for simplifying thermal efficiency planning in cities

Biased Information Passing Between Subsystems Over Time in Complex System Design

On Using Ilities of Non-Functional Properties for Subsystems and Components

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