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Journal articles on the topic 'Tradespace Exploration'

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Author: Grafiati

Published: 14 March 2023

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1

Ross, Adam M., and Daniel E. Hastings. "11.4.3 The Tradespace Exploration Paradigm." INCOSE International Symposium 15, no. 1 (July 2005): 1706–18. http://dx.doi.org/10.1002/j.2334-5837.2005.tb00783.x.

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2

Ross, Adam M., David B. Stein, and Daniel E. Hastings. "Multi-Attribute Tradespace Exploration for Survivability." Journal of Spacecraft and Rockets 51, no. 5 (September 2014): 1735–52. http://dx.doi.org/10.2514/1.a32789.

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3

Smirnov, Dmitry, and Alessandro Golkar. "Stirling Engine Systems Tradespace Exploration Framework." Procedia Computer Science 44 (2015): 558–67. http://dx.doi.org/10.1016/j.procs.2015.03.010.

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4

Baylot, E. Alex, Drew Kelley, James Richards, and Deanna Hardin. "Introducing Cost Models to Conceptual Tradespace Exploration." INCOSE International Symposium 28, no. 1 (July 2018): 16–29. http://dx.doi.org/10.1002/j.2334-5837.2018.00464.x.

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5

Fitzgerald, Matthew E., and Adam M. Ross. "Recommendations for Framing Multi-Stakeholder Tradespace Exploration." INCOSE International Symposium 26, no. 1 (July 2016): 2376–90. http://dx.doi.org/10.1002/j.2334-5837.2016.00301.x.

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6

Davison, Peter, Bruce G. Cameron, and Edward F. Crawley. "Tradespace exploration of in-space communications network architectures." Technology Analysis & Strategic Management 29, no. 6 (August 29, 2016): 583–99. http://dx.doi.org/10.1080/09537325.2016.1217322.

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7

Spero, Eric, Michael P. Avera, Pierre E. Valdez, and Simon R. Goerger. "Tradespace Exploration for the Engineering of Resilient Systems." Procedia Computer Science 28 (2014): 591–600. http://dx.doi.org/10.1016/j.procs.2014.03.072.

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8

Specking, Eric, Gregory Parnell, Edward Pohl, and Randy Buchanan. "Evaluating a Set-Based Design Tradespace Exploration Process." Procedia Computer Science 153 (2019): 185–92. http://dx.doi.org/10.1016/j.procs.2019.05.069.

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9

Bhattacharya, Saikath, Vidhyashree Nagaraju, Eric Spero, Anindya Ghoshal, and Lance Fiondella. "Incorporating quantitative reliability engineering measures into tradespace exploration." Research in Engineering Design 29, no. 4 (July 13, 2018): 589–603. http://dx.doi.org/10.1007/s00163-018-0293-8.

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10

Specking, Eric, Gregory Parnell, Edward Pohl, and Randy Buchanan. "Early Design Space Exploration with Model-Based System Engineering and Set-Based Design." Systems 6, no. 4 (December 17, 2018): 45. http://dx.doi.org/10.3390/systems6040045.

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Adequately exploring the tradespace in the early system design phase is important to determine the best design concepts to pursue in the next life cycle stage. Tradespace exploration (TSE) often uses trade-off analysis. Set-based design (SBD) methods, compared to traditional point-based design, explore significantly more designs. An integrated framework with model-based system engineering (MBSE) and a life cycle cost model enables design evaluation in near real-time. This study proposes an early design phase SBD methodology and demonstrates how SBD enabled by an integrated framework with MBSE and life cycle cost provides an enhanced TSE that can inform system design requirements and help decision makers select high performing designs at an affordable cost. Specifically, this paper (1) provides an overview of TSE and SBD, (2) describes the Integrated Trade-off Analysis Framework, (3) describes a methodology to implement SBD in the early design phase, and (4) demonstrates the techniques using an unmanned aerial vehicle case study. We found that the Integrated Trade-off Analysis Framework informs requirement development based upon how the requirements affect the feasible tradespace. Additionally, the integrated framework that uses SBD better explores the design space compared to traditional methods by finding a larger set of feasible designs early in the design process.

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11

Small, Colin, Gregory S. Parnell, Ed Pohl, Simon R. Goerger, Matthew Cilli, and Eric Specking. "Demonstrating set-based design techniques: an unmanned aerial vehicle case study." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 17, no. 4 (September 10, 2019): 339–55. http://dx.doi.org/10.1177/1548512919872822.

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The Engineered Resilient Systems research program seeks to improve decision making in the Analysis of Alternatives process by leveraging model-based engineering (MBE) early in the design process to develop more resilient systems. Traditional tradespace exploration using point-based design often converges quickly to an initial baseline design concept with subsequent engineering changes to modify the design. However, this process can lead to significant cost growth if the initial concept is not able to meet requirements or if the revised design is not affordable. Enabled by MBE, set-based design (SBD) considers sets of all possible design concepts and down-selects design concepts to converge to a final design using insights into design trade-off analysis, modeling and simulation, and test data. Using a notional unmanned aerial vehicle case study with low-fidelity physics-based models and an open source Excel® add-in called SIPmath©, this research implements an integrated MBE trade-off analytics framework that simultaneously generates numerous SBDs using parametric performance and cost models and evaluates the designs in the value and cost tradespace. In addition, this research explores incorporating resilience quantification and uncertainty into SBD trade-off analysis. Future research is needed to validate the use of SBD with low-fidelity models for tradespace exploration in early system design.

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12

Fitzgerald, Matthew E., and Adam M. Ross. "Controlling for Framing Effects in Multi-stakeholder Tradespace Exploration." Procedia Computer Science 28 (2014): 412–21. http://dx.doi.org/10.1016/j.procs.2014.03.051.

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13

Richards, Matthew G., Adam M. Ross, Nirav B. Shah, and Daniel E. Hastings. "Metrics for Evaluating Survivability in Dynamic Multi-Attribute Tradespace Exploration." Journal of Spacecraft and Rockets 46, no. 5 (September 2009): 1049–64. http://dx.doi.org/10.2514/1.41433.

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14

Specking, Eric, Nicholas Shallcross, Gregory S. Parnell, and Edward Pohl. "Quantitative Set-Based Design to Inform Design Teams." Applied Sciences 11, no. 3 (January 29, 2021): 1239. http://dx.doi.org/10.3390/app11031239.

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System designers, analysts, and engineers use various techniques to develop complex systems. A traditional design approach, point-based design (PBD), uses system decomposition and modeling, simulation, optimization, and analysis to find and compare discrete design alternatives. Set-based design (SBD) is a concurrent engineering technique that compares a large number of design alternatives grouped into sets. The existing SBD literature discusses the qualitative team-based characteristics of SBD, but lacks insights into how to quantitatively perform SBD in a team environment. This paper proposes a qualitative SBD conceptual framework for system design, proposes a team-based, quantitative SBD approach for early system design and analysis, and uses an unmanned aerial vehicle case study with an integrated model-based engineering framework to demonstrate the potential benefits of SBD. We found that quantitative SBD tradespace exploration can identify potential designs, assess design feasibility, inform system requirement analysis, and evaluate feasible designs. Additionally, SBD helps designers and analysts assess design decisions by providing an understanding of how each design decision affects the feasible design space. We conclude that SBD provides a more holistic tradespace exploration process since it provides an integrated examination of system requirements and design decisions.

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15

Battat, Jonathan A., Bruce Cameron, Alexander Rudat, and Edward F. Crawley. "Technology Decisions Under Architectural Uncertainty: Informing Investment Decisions Through Tradespace Exploration." Journal of Spacecraft and Rockets 51, no. 2 (March 2014): 523–32. http://dx.doi.org/10.2514/1.a32562.

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16

Davison, Peter, Demetrios Kellari, Edward F. Crawley, and Bruce G. Cameron. "Communications satellites: Time expanded graph exploration of a tradespace of architectures." Acta Astronautica 115 (October 2015): 442–51. http://dx.doi.org/10.1016/j.actaastro.2015.05.017.

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17

Chattopadhyay, Debarati, Adam M. Ross, and Donna H. Rhodes. "Combining Attributes for Systems of Systems in Multi-Attribute Tradespace Exploration." INSIGHT 13, no. 2 (July 2010): 31–38. http://dx.doi.org/10.1002/inst.201013231.

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18

Ross, Adam M., Daniel E. Hastings, Joyce M. Warmkessel, and Nathan P. Diller. "Multi-Attribute Tradespace Exploration as Front End for Effective Space System Design." Journal of Spacecraft and Rockets 41, no. 1 (January 2004): 20–28. http://dx.doi.org/10.2514/1.9204.

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19

Fitzgerald, Matthew E., and Adam M. Ross. "Guiding Cooperative Stakeholders to Compromise Solutions Using an Interactive Tradespace Exploration Process." Procedia Computer Science 16 (2013): 343–52. http://dx.doi.org/10.1016/j.procs.2013.01.036.

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20

Spero, Eric, Christina L. Bloebaum, Brian J. German, Art Pyster, and Adam M. Ross. "A Research Agenda for Tradespace Exploration and Analysis of Engineered Resilient Systems." Procedia Computer Science 28 (2014): 763–72. http://dx.doi.org/10.1016/j.procs.2014.03.091.

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21

Sitterle, Valerie B., Dane F. Freeman, Simon R. Goerger, and Tommer R. Ender. "Systems Engineering Resiliency: Guiding Tradespace Exploration within an Engineered Resilient Systems Context." Procedia Computer Science 44 (2015): 649–58. http://dx.doi.org/10.1016/j.procs.2015.03.013.

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22

Fitzgerald, Matthew E., and Adam M. Ross. "Artificial intelligence analytics with Multi-Attribute Tradespace Exploration and Set-Based Design." Procedia Computer Science 153 (2019): 27–36. http://dx.doi.org/10.1016/j.procs.2019.05.052.

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23

Qiao, Li, Mahmoud Efatmaneshnik, and Michael Ryan. "A Combinatorial Approach to Tradespace Exploration of Complex Systems: A CubeSat Case Study." INCOSE International Symposium 27, no. 1 (July 2017): 763–79. http://dx.doi.org/10.1002/j.2334-5837.2017.00392.x.

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24

Palermo, Gianluca, Alessandro Golkar, and Paolo Gaudenzi. "Earth Orbiting Support Systems for commercial low Earth orbit data relay: Assessing architectures through tradespace exploration." Acta Astronautica 111 (June 2015): 48–60. http://dx.doi.org/10.1016/j.actaastro.2015.02.011.

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25

Salado, Alejandro. "Defining Better Test Strategies with Tradespace Exploration Techniques and Pareto Fronts: Application in an Industrial Project." Systems Engineering 18, no. 6 (November 2015): 639–58. http://dx.doi.org/10.1002/sys.21332.

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26

Crisp, N. H., K. L. Smith, and P. M. Hollingsworth. "An integrated design methodology for the deployment of constellations of small satellites." Aeronautical Journal 123, no. 1266 (July 24, 2019): 1193–215. http://dx.doi.org/10.1017/aer.2019.57.

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ABSTRACTA growing interest in constellations of small satellites has recently emerged due to the increasing capability of these platforms and their reduced time and cost of development. However, in the absence of dedicated launch services for these systems, alternative methods for the deployment of these constellations must be considered which can take advantage of the availability of secondary-payload launch opportunities. Furthermore, a means of exploring the effects and tradeoffs in corresponding system architectures is required. This paper presents a methodology to integrate the deployment of constellations of small satellites into the wider design process for these systems. Using a method of design-space exploration, enhanced understanding of the tradespace is supported , whilst identification of system designs for development is enabled by the application of an optimisation process. To demonstrate the method, a simplified analysis framework and a multiobjective genetic algorithm are implemented for three mission case-studies with differing application. The first two cases, modelled on existing constellations, indicate the benefits of design-space exploration, and possible savings which could be made in cost, system mass, or deployment time. The third case, based on a proposed Earth observation nanosatellite constellation, focuses on deployment following launch using a secondary-payload opportunity and demonstrates the breadth of feasible solutions which may not be considered if only point-designs are generated by a priori analysis. These results indicate that the presented method can support the development of future constellations of small satellites by improving the knowledge of different deployment strategies available during the early design phases and through enhanced exploration and identification of promising design alternatives.

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27

Varaklis, Kalli, Mark G. Parker, Jordan S. Peck, and Robert G. Bing-You. "Aligning Strategic Interests in an Academic Medical Center: A Framework for Evaluating GME Expansion Requests." Journal of Graduate Medical Education 11, no. 1 (February 1, 2019): 85–91. http://dx.doi.org/10.4300/jgme-d-18-00730.1.

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ABSTRACT Background In 2017, the Maine Medical Center Graduate Medical Education Committee received an unprecedented number of requests (n = 18) to start new graduate medical education (GME) programs or expand existing programs. There was no process by which multiple programs could be prioritized to compete for scarce GME resources. Objective We developed a framework to strategically assess and prioritize GME program expansion requests to yield the greatest benefits for patients, learners, and the institution as well as to meet regional and societal priorities. Methods A systems engineering methodology called tradespace exploration was applied to a 6-step process to identify relevant categories and metrics. Programs' final scores were peer evaluated, and prioritization recommendations were made. Correlation analysis was used to evaluate the relevance of each category to final scores. Stakeholder feedback was solicited for process refinement. Results Five categories relevant to GME expansion were identified: institutional priorities, health care system priorities, regional and societal needs, program quality, and financial considerations. All categories, except program quality, correlated well with final scores (R2 range 0.413–0.662). Three of 18 requested programs were recommended for funding. A stakeholder survey revealed that almost half of respondents (48%, 14 of 29) agreed that the process was unbiased and inclusive. Focus group feedback noted that the process had been rigorous and deliberate, although communication could have been improved. Conclusions Applying a systems engineering approach to develop institution-specific metrics for assessing GME expansion requests provided a reproducible framework, allowing consideration of institutional, health care system, and regional societal needs, as well as program quality and funding considerations.

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28

MacCalman, Alex D., Paul T. Beery, and Eugene P. Paulo. "A Systems Design Exploration Approach that Illuminates Tradespaces Using Statistical Experimental Designs." Systems Engineering 19, no. 5 (September 2016): 409–21. http://dx.doi.org/10.1002/sys.21352.

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29

Li, Kevin T., Christian A. Hofmann, Harald Reder, and Andreas Knopp. "A Techno-Economic Assessment and Tradespace Exploration of Low Earth Orbit Mega-Constellations." IEEE Communications Magazine, 2022, 1–7. http://dx.doi.org/10.1109/mcom.001.2200312.

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30

Xu, Peng, Alejandro Salado, and Xinwei Deng. "A Parallel Tempering Approach for Efficient Exploration of the Verification Tradespace in Engineered Systems." IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 1–13. http://dx.doi.org/10.1109/tsmc.2022.3152784.

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31

Gallarno, George, Jeremy Muniz, Gregory S. Parnell, Edward A. Pohl, and Jingxian Wu. "Development and assessment of a resilient telecoms system." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, January 11, 2023, 154851292211437. http://dx.doi.org/10.1177/15485129221143791.

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Emergency telecommunication infrastructure is essential for residents and emergency responders during natural disasters to coordinate life-saving and life-preserving efforts. Ensuring resilience of the emergency telecommunication infrastructure is of critical importance for regions with an increased likelihood of natural disasters. We developed an integrated modeling framework for assessing emergency telecommunication systems. The framework used performance models to assess coverage and surge capabilities for a given system architecture. The performance models assess the telecom system value using a multiple objective decision analysis value model with stake-holder and technology performance measures. After constructing a life cycle cost model for emergency telecommunication systems, we conducted an illustrative value versus cost trade-off analysis using three alternative decision frames. The decision analysis framework allows for exploration of the system design tradespace so that decision-makers can select the best emergency telecom architecture using a defensible and transparent, performance-driven methodology.

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32

Martin, Jay D. "Computational Improvements to Estimating Kriging Metamodel Parameters." Journal of Mechanical Design 131, no. 8 (July 9, 2009). http://dx.doi.org/10.1115/1.3151807.

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The details of a method to reduce the computational burden experienced while estimating the optimal model parameters for a Kriging model are presented. A Kriging model is a type of surrogate model that can be used to create a response surface based a set of observations of a computationally expensive system design analysis. This Kriging model can then be used as a computationally efficient surrogate to the original model, providing the opportunity for the rapid exploration of the resulting tradespace. The Kriging model can provide a more complex response surface than the more traditional linear regression response surface through the introduction of a few terms to quantify the spatial correlation of the observations. Implementation details and enhancements to gradient-based methods to estimate the model parameters are presented. It concludes with a comparison of these enhancements to using maximum likelihood estimation to estimate Kriging model parameters and their potential reduction in computational burden. These enhancements include the development of the analytic gradient and Hessian for the log-likelihood equation of a Kriging model that uses a Gaussian spatial correlation function. The suggested algorithm is similar to the SCORING algorithm traditionally used in statistics.

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