Short answer

Incorporate computational tools to systematically explore design variations, using objective data and iterative user feedback to guide the search for optimal solutions.

Field: Modelling
Source: ScholarsArchive (Brigham Young University) (2010)
Method: Computational modelling and simulation combined with human-in-the-loop evaluation.
Evidence: Strong effect

Leveraging computational tools to explore a wider range of design possibilities in the early conceptual stages can lead to more optimal and potentially undiscovered solutions. This modelling research insight is drawn from a 2010 study published in ScholarsArchive (Brigham Young University). Using Computational modelling and simulation combined with human-in-the-loop evaluation., researchers explored how this design variable affects real-world outcomes. The key design takeaway: Incorporate computational tools to systematically explore design variations, using objective data and iterative user feedback to guide the search for optimal solutions.

Study

ModellingHigh ImpactStrong effect

Computational Assistance Accelerates Conceptual Design Exploration

Leveraging computational tools to explore a wider range of design possibilities in the early conceptual stages can lead to more optimal and potentially undiscovered solutions.

ScholarsArchive (Brigham Young University) · 2010

Key Findings

01Computational assistance enables a more comprehensive search of the design space than manual methods alone.
02Integrating objective performance metrics with subjective designer preferences leads to more well-rounded design solutions.
03The methodology can uncover optimal designs that might be missed through traditional design exploration.

Application

Design takeaway

Incorporate computational tools to systematically explore design variations, using objective data and iterative user feedback to guide the search for optimal solutions.

How to apply

Develop parameterized models of design components and use optimization algorithms to generate variations, then present these to users for feedback to refine the design direction.

Project actions

01Consider using parametric modelling to define design variations.
02Think about how to gather and incorporate user preferences into your design process.

Method & Evidence

AimHow can computational assistance be integrated into the conceptual design process to facilitate a more thorough exploration of design possibilities, considering both objective performance and subjective designer preferences?

MethodComputational modelling and simulation combined with human-in-the-loop evaluation.

ProcedureAn initial set of designs is parameterized and used in a semi-automated search that recombines features. Designs are evaluated using physics-based models and subjective designer feedback, which is used to build an evolving preference model. This multiobjective approach searches for optimal designs by treating both objective and subjective evaluations as goals.

ContextConceptual product design.

Variables

IVComputational assistance in design exploration.

DVNumber of design possibilities explored, optimality of selected designs, designer satisfaction.

CVComplexity of the design problem, accuracy of physics-based models, consistency of designer feedback.

Strengths & Limitations

Strengths

+Systematic exploration of a large design space.
+Integration of objective and subjective design criteria.

Limitations

The complexity of setting up computational models and gathering consistent user feedback can be challenging within a limited project scope.

Reliability & validity

Reliability would depend on the consistency of the computational generation process and the stability of the preference model. Validity would be assessed by how well the computationally-derived optimal designs perform in objective tests and user satisfaction.

Think critically

To what extent can subjective designer preferences be accurately modelled computationally, and what are the risks of over-reliance on such models?

Design Principles

"Augment human creativity with computational exploration to broaden the scope of design solutions."

In the initial phases of product development, the breadth of explored design concepts significantly impacts the final product's success. This research highlights how computational assistance can augment a designer's ability to systematically search through a vast design space, balancing objective performance with subjective user preferences.

What This Means for Your Design

Computers can help designers explore lots of different ideas quickly, making sure the designs work well and that people like them.

How to use in your project

1.Reference this research when discussing methods for exploring design concepts and incorporating user feedback in your design project.

Add to My Project

Quick Cite

Paragraph starter

This research demonstrates how computational assistance can significantly enhance the exploration of design possibilities during the conceptual phase. By parameterizing initial designs and employing semi-automated recombination, designers can systematically generate and evaluate a wider array of solutions. The integration of objective physics-based models with subjective, human-driven preference evaluations allows for a more holistic approach to optimization, potentially uncovering novel and superior design outcomes that might otherwise be overlooked.

Source

ScholarsArchive (Brigham Young University)

A Computationally-assisted Methodology for Rapid Exploration of Design Possibilities in Conceptual Design

journal · 2010

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Questions About This Research

What does the research say about computational assistance accelerates conceptual design exploration?: Incorporate computational tools to systematically explore design variations, using objective data and iterative user feedback to guide the search for optimal solutions. Evidence: ScholarsArchive (Brigham Young University) (2010).
Why does "Computational Assistance Accelerates Conceptual Design Exploration" matter for design?: In the initial phases of product development, the breadth of explored design concepts significantly impacts the final product's success. This research highlights how computational assistance can augment a designer's ability to systematically search through a vast design space, balancing objective performance with subjective user preferences.
How can designers apply this research?: Incorporate computational tools to systematically explore design variations, using objective data and iterative user feedback to guide the search for optimal solutions.
What were the main findings?: Computational assistance enables a more comprehensive search of the design space than manual methods alone.. Integrating objective performance metrics with subjective designer preferences leads to more well-rounded design solutions.. The methodology can uncover optimal designs that might be missed through traditional design exploration.
What research method was used?: Computational modelling and simulation combined with human-in-the-loop evaluation..
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2010 journal from ScholarsArchive (Brigham Young University).
What should I do differently in my next project?: Develop parameterized models of design components and use optimization algorithms to generate variations, then present these to users for feedback to refine the design direction.
What are the limitations?: The effectiveness of the preference model is dependent on the quality and consistency of designer feedback. The computational models used for objective evaluation must accurately reflect real-world performance.