Signomial Programming Models for Aircraft Design

Kirschen, Philippe G.; Burnell, Edward; Hoburg, Warren

doi:10.2514/6.2016-2003

Cited by 16 publications

(14 citation statements)

References 13 publications

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“…The design model underlying this graphical interface was based on the "SimPleAC" GPkit model for passenger aircraft, [33] itself a condensed version of previous GPkit models for commercial aircraft [34,35] that had been co-developed with the robust optimization library [3]. While SimPleAC relies on approximately 40 different variables to minimize the fuel consumption, participants were only given control of four to five variables.…”

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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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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“…This method of determining failure rates is similar to best-practices Monte Carlo simulations. The design model underlying this graphical interface was based on the "SimPleAC" GPkit model for passenger aircraft, [32] itself a condensed version of previous GPkit models for commercial aircraft [33,34]…”

Section: Experimental Interfacementioning

confidence: 99%

Early-Stage Uncertainty: Effects of Robust Convex Optimization on Design Exploration

Pillai

Burnell

Wang

et al. 2020

Volume 8: 32nd International Conference on Design Theory and Methodology (DTM)

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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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“…Relatively cool air is bled from the compression system, bypassing the combustion process, and enters the hollow blade or vane. The gas temperature can therefore be even higher than the melting temperature of the blade [3]. After picking up heat from the blade/vane, the cooling air is dumped into the main gas stream.…”

Section: The Matmentioning

confidence: 99%

“…The thickness of blade is estimated to be 0.5 in. The fan blade the width of each blade, , is determined below [3]. · # · # 15.786…”

mentioning

confidence: 99%

Sustainable Design of Turbofan Engine: A Computer-Aided Design and Finite Element Analysis

Jha¹

2018

JMEA

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A compressive design and analysis of a turbofan engine is presented in this paper. The components of jet engine have been analyzed based on mechanical design concept. An attempt has been to select materials based on sustainability and green design considerations. The energy content (e) of the materials has been one of the parameters for material selection. The choice of material has a substantial impact on cost, manufacturing process, and the life cycle efficiency. All components nose cone, fan blade, inlet shaft, including compressor has been solid modeled using Siemens NX 11.0 CAD software. The finite element analysis of every component was performed and found safe. A tolerance analysis was performed before assembly of the turbofan engine. A numerical analysis was completed on blade and inlet geometries to determine a more efficient turbofan engine. Thermal analysis was executed on the cone and suitable corrections were made. Finally, the cost and the total energy were estimated to show how much energy is needed to manufacture a turbofan jet engine.

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Signomial Programming Models for Aircraft Design

Cited by 16 publications

References 13 publications

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

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

Early-Stage Uncertainty: Effects of Robust Convex Optimization on Design Exploration

Sustainable Design of Turbofan Engine: A Computer-Aided Design and Finite Element Analysis

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