A Research Agenda for Tradespace Exploration and Analysis of Engineered Resilient Systems

Spero, Eric; Bloebaum, Christina; German, Brian; Pyster, Art; Ross, Adam M.

doi:10.1016/j.procs.2014.03.091

Cited by 21 publications

(9 citation statements)

References 2 publications

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“…This drives the need for accelerating the transformation of a model-based discipline (Peterson, 2016). Given computational power and software environments available today, a new level of sophistication is possible for accomplishing systems engineering tasks (e.g., Spero et al, 2014). Data sets are very large and a system model is comprised of an integrated set of models of various types (e.g., discrete event simulation, agent-based models, dynamic models and simulations, etc.).…”

Section: Discussionmentioning

confidence: 99%

A Vision for Human‐model Interaction in Interactive Model‐centric Systems Engineering

Rhodes¹,

Ross²

2017

INSIGHT

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The Interactive Model‐Centric Systems Engineering (IMCSE) research program arises from the unique opportunity to investigate the various aspects of humans interacting with models and model‐generated data, in the context of systems engineering practice. This paper explores the dimensions of this interaction and shares findings from a recent workshop organized to seed the initial research agenda. Many open areas of inquiry exist; examples include: use cases for stakeholders interacting with models; how multiple stakeholders interact with each other using models and model generated information; facets of human interaction with visualizations and large data sets; and underlying fundamentals such as model purpose and model handling. Realizing the envisioned future for an interactive model‐centric systems engineering experience will require new knowledge and ways of working, and innovation in constructs and technologies. A shared research agenda is proposed as a means to provide a common frame for alignment and synergies of research across the community. Research needs and progress are discussed and several ongoing research projects are briefly discussed.

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Section: Discussionmentioning

confidence: 99%

A Vision for Human‐model Interaction in Interactive Model‐centric Systems Engineering

Rhodes¹,

Ross²

2017

INSIGHT

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“…A tradespace refers to the potential solution space (Ross, Hastings et al 2004), usually stored in databases consists of a set of multiple design alternatives, data attributes, system parameters that designers can evaluate to satisfy various design objects (Brantley, McFadden et al 2002). A tradespace is a repository of qualitative and quantitative information that can be queried, filtered, and visualized in order to help reveal information to a person making a decision (Spero, Bloebaum et al 2014). Simply put, design tradespace exploration is a process of knowledge discovery of the design trade space.…”

Section: Tradespace Explorationmentioning

confidence: 99%

A Combinatorial Approach to Tradespace Exploration of Complex Systems: A CubeSat Case Study

Qiao

Efatmaneshnik

Ryan

2017

INCOSE International Symp

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This paper presents a novel approach to tradespace exploration for large‐scale and multi‐dimensional tradespace, which reduce the cognitive workload in addressing numerous design options to a manageable level. Our approach enables exploration by classifying subsets of design solutions or designs of interest, which will in turn facilitate and accelerate the decision making process. As the design tradespace for products such as CubeSats is naturally large‐scale and multi‐dimensional, the proposed approach integrates computational data clustering algorithms with multi‐dimensional scaling visualization. In the proposed approach, k‐medoids clustering provides a grouping model where similar designs are grouped; uncovering hidden patterns and features within datasets; and the principal component analysis allow users to visualize design samples on two‐dimensional scatter plots. A case study tradespace consisting of 1200+ CubeSat design alternatives is used to demonstrate how this approach supports knowledge discovery of design options through tradespace exploration of multi‐dimensional data.

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“…In addition, academia is also pursuing consolidation efforts and a research agenda has been proposed to advance the capabilities, maturity and infusion of tradespace exploration for conceptual design and system architecture [Spero et al., ]. In particular, four areas of work are anticipated: “Broadening, populating, and managing the tradespace,” which is primarily focused on characterizing and modeling the different elements that build up the tradespace, such as for example “ilities,” uncertainty, or historical data. “Linking the tradespace,” which is primarily focused on enabling technology compatibility, that is tradespace tooling. “Searching, exploring, and analyzing the tradespace,” which is primarily focused on improving the search process and the visualization mechanisms of tradespace exploration. “Acting on the tradespace,” which is primarily focused on harmonization activities in terms of foundations and processes. …”

Section: Literature Reviewmentioning

confidence: 99%

Section: Tradespace Exploration and Pareto Frontiersmentioning

confidence: 99%

“…Similar boundary conditions favored the emergence of tradespace exploration techniques and Pareto frontiers for conceptual design [Ross and Hastings, 2005]. Academia has consistently proven the benefits of such techniques over point designs to achieve more affordable solutions [Ross et al, 2004;Ryan, Sarkani and Mazzuchi, 2014;Spero et al, 2014b;Davison, Cameron and Crawley, 2015]. In fact, tradespace exploration is slowly becoming part of standards in industrial contexts as well [Randii et al, 2013].…”

Section: Introductionmentioning

confidence: 99%

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Defining Better Test Strategies with Tradespace Exploration Techniques and Pareto Fronts: Application in an Industrial Project

Salado

2015

Syst. Engin.

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Test strategies are usually defined following a point-design like method. Starting with a set of verification requirements and programmatic constraints, and usually with a generic test sequence, a baseline test approach is defined. Then, the baseline is optimized until an acceptable strategy is found. Academia has consistently shown however the benefits of using tradespace exploration techniques instead of pointbased designs. Some industrial applications seem to corroborate such findings. Yet, both academia and industry have limited the use of tradespace exploration techniques to selecting design concepts. This paper proposes that broadening the use of tradespace exploration techniques and Pareto frontiers to other activities in the field of systems engineering domain yields similar benefits. In particular, this paper presents the actual application of tradespace exploration techniques and Pareto frontiers in an industrial context to select a test strategy for a system. This paper provides thus three main contributions. First, the paper demonstrates that tradespace exploration and Pareto frontiers can be beneficial beyond concept selection. Second, the paper presents a process to use tradespace exploration techniques and Pareto frontiers for selecting test strategies. Finally, the paper showcases the application of the proposed technique and process in a real industrial setup, which yielded a number of lessons learnt. C⃝ 2016 Wiley Periodicals, Inc. Syst Eng 18: 639-658, 2015

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A Research Agenda for Tradespace Exploration and Analysis of Engineered Resilient Systems

Cited by 21 publications

References 2 publications

A Vision for Human‐model Interaction in Interactive Model‐centric Systems Engineering

A Vision for Human‐model Interaction in Interactive Model‐centric Systems Engineering

A Combinatorial Approach to Tradespace Exploration of Complex Systems: A CubeSat Case Study

Defining Better Test Strategies with Tradespace Exploration Techniques and Pareto Fronts: Application in an Industrial Project

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