Integrating Design and Optimization Tools: A Designer Centered Study

Burnell, Edward; Stern, Michael; Flooks, Ana; Yang, Maria C.

doi:10.1115/detc2017-68307

Cited by 3 publications

(3 citation statements)

References 16 publications

(16 reference statements)

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“…Some participant groups in Breneman's excellent study of collaborative satellite design show the scattered focus and poor outcomes that can occur in such contexts [3]. Similarly, lab experiments with convex optimization have shown with individual novice users that the underlying mathematics of a design model can affect design outcomes [10], but have struggled to show any change in this effect with pairs of novice users [37].…”

Section: Methodsmentioning

confidence: 99%

“…The influence of a design model's material is felt especially in a a design process' earliest stages, where the work is predominantly conceptual and lacks physical prototypes to reference [21,57]. The effects of earlystage design model materials are typically examined through experimental outcomes [10,66,67], but some materials have not been studied in this way.…”

Section: Introductionmentioning

confidence: 99%

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GPkit: A Human-Centered Approach to Convex Optimization in Engineering Design

Burnell

Damen

Hoburg

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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We present GPkit, a Python toolkit for Geometric and Signomial Programming that prioritizes explainability and incremental complexity. GPkit was designed through an ethnographic approach in the firms, classrooms, and research labs where it became part of the fabric of daily engineering work. Organizations have approached GPkit both in ways which centralize design work and in ways which distribute it, usecases which emerged from and inspired new toolkit features. This twoway flow between mathematical structure and practitioner knowledge resulted in several novel contributions to the formulation and interpretation of convex programs and to our understanding of early-stage engineering design. For example, dual solutions (often considered incidental) can be more valuable to a design process than the "optimal design" itself, and we present novel algorithms and design methods based on this insight.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GPkit: A Human-Centered Approach to Convex Optimization in Engineering Design

Burnell

Damen

Hoburg

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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“…They were tasked with finding designs in three "reward regions" and to find designs on the final combined Pareto frontier; participants received greater compensation depending on their performance on these metrics. Each participant was given a ten minute tutorial, 30 min to complete the design challenge, and 10 min to complete a short survey about their experience using the tool after the experiment, based on surveys used in similar experiments [32]. The code used for this experiment is available in an open source GitHub repository.…”

Section: Human-subjects Experimentsmentioning

confidence: 99%

Effects of Robust Convex Optimization on Early-Stage Design Space Exploratory Behavior

Pillai

Burnell

Wang

et al. 2020

Journal of Mechanical Design

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Engineers design for an inherently uncertain world. In the early stages of design processes, they commonly account for such uncertainty either by manually choosing a specific worst-case and multiplying uncertain parameters with safety factors, or by using Monte Carlo simulations to estimate the probabilistic boundaries in which their design is feasible. The safety factors of this first practice are determined by industry and organizational standards, providing a limited account of uncertainty; the second practice is time intensive, requiring the development of separate testing infrastructure. In theory, robust optimization provides an alternative, allowing set based conceptualizations of uncertainty to be represented during model development as optimizable design parameters. How these theoretical benefits translate to design practice has not previously been studied. In this work, we analyzed present use of geometric programs as design models in the aerospace industry to determine the current state-of-the-art, then conducted a human-subjects experiment to investigate how various mathematical representations of uncertainty affect design space exploration. We found that robust optimization led to far more efficient explorations of possible designs with only small differences in an experimental participant's understanding of their model. Specifically, the Pareto frontier of a typical participant using robust optimization left less performance “on the table” across various levels of risk than the very best frontiers of participants using industry-standard practices.

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