Problems, solutions, and design itself have been framed as spaces in design research. Visualising the design space and how designers explore it, can give insight into the design process. This paper reports on a novel method for creating Design Space Visualisations (DS-Viz) that generates 2D and 3D representations of design spaces. We show how DS-Viz can be used to investigate designer behaviour, design processes and outcomes using a game-based design activity as an example. We discuss DS-Viz implications for design research highlighting potential benefits to design education and practice.

Software implementations of traditional engineering design methods can potentially enrich the original methods. A study was conducted to better understand how concept generation can be facilitated using software. Participants of the study were asked to generate concepts using either specialized software, or by using traditional means, for applying function-means modeling and morphological matrices. A concept concretization metric was used to evaluate the results, which indicated that there are both positive and negative aspects of performing concept generation using specialized software.

Computational design tools allow the generation of vast numbers of possible designs, entrusting the human designer with describing constraints or specifications to guide exploration of the design space. Designers can have many different decision considerations when conducting this type of exploration, including form, function, users, or context. In this work, we investigate strategies that emerge when people are tasked with exploring a large design space within either a non-immersive (2D) or immersive (VR) interface and equipped with action-based interactions to set or envision specifications related to their considerations. Results from a 28 participant user study uncovers that people have varying strategies to enact their decision considerations that are not unique to the type of interface. However, the interfaces differ in perceptions of enabling breadth or depth of exploration holistically, with preference towards 2D interfaces to compare options, and VR to understand single designs. These results have implications for the user experience of systems that allow designers to explore the outputs of large design spaces, both at the interaction and interface levels.

The electrification of the commuter aircraft is instrumental in the development of novel propulsion systems. The scope of this work aims to explore the design space of a parallel hybrid-electric configuration with an entry into service date of 2030 and beyond and determine the impact of other disciplines on conceptual design, such as components positioning, aircraft stability and structural integrity. Three levels of conceptual sizing are applied and linked with a parametric aircraft geometry tool, to generate the aircraft’s geometry and position the components. Subsequently, the structural optimisation of the wing box is performed, providing the centre of gravity of the components placed inside the wing, that minimise the induced stresses. Furthermore, the stability and trim analysis follow, with the former being highly affected by the positioning of components. Results are compared to a similar aircraft with entry into service technology of 2014 and it is indicated that in terms of block fuel reduction the total electrification benefit increases with the increase of degree of hybridisation, if aircraft mass is kept constant. On the other hand, if battery specific energy is kept constant, similar block fuel reduction is possible with lower hybridisation degrees. The potential for improvement in terms of carbon dioxide emissions and block fuel reduction ranges from 15.73% to 21.44% compared to the conventional aircraft, for levels of battery specific energy of 0.92 and 1.14 kWh/kg respectively. Finally, the component positioning evaluation indicates a maximum weight limitation of 240 kg for the addition of an aft boundary layer ingestion fan to a tube and wing aircraft configuration, without compromising the aircraft static stability.

Sustainability considerations are traditionally difficult to trade-off with technical and business requirements in an early design phase. Hence, design teams need support to reflect early on in the process, on how sustainability may affect profitability and customer value fulfilment in the long term. The commoditisation of modelling and simulation techniques points to gamification and serious gaming as emerging approaches to raise awareness among the design team – as well as users and stakeholders – of the expected behaviour of a solution along its life cycle. The objective of this paper is to explore how serious games can be used to inform decision-makers about the value versus cost implications of being (or not being) ‘sustainability compliant’ when designing products and systems. The paper initially presents the findings from a descriptive study focused on the definition of ‘design support’ intended to raise sustainability awareness through serious gaming. It further describes the development, application and testing of one of such games for material selection in the aerospace industry.

Generative neural networks (GNNs) have successfully used human-created designs to generate novel 3D models that combine concepts from disparate known solutions, which is an important aspect of design exploration. GNNs automatically learn a parameterization (or latent space) of a design space, as opposed to alternative methods that manually define a parameterization. However, GNNs are typically not evaluated using an explicit notion of physical performance, which is a critical capability needed for design. This work bridges this gap by proposing a method to extract a set of functional designs from the latent space of a point cloud generating GNN, without sacrificing the aforementioned aspects of a GNN that are appealing for design exploration. We introduce a sparsity preserving cost function and initialization strategy for a genetic algorithm (GA) to optimize over the latent space of a point cloud generating autoencoder GNN. We examine two test cases, an example of generating ellipsoid point clouds subject to a simple performance criterion and a more complex example of extracting 3D designs with a low coefficient of drag. Our experiments show that the modified GA results in a diverse set of functionally superior designs while maintaining similarity to human-generated designs in the training data set.

In mass customization, software configurators enable novice end-users to design customized products and services according to their needs and preferences. However, traditional configurators hardly provide an engaging experience while avoiding the burden of choice. We propose a Design Participation Model to facilitate navigating the design space, based on two modules. Modeler enables designers to create customizable designs as parametric models, and Navigator subsequently permits novice end-users to explore these designs. While most parametric designs support direct manipulation of low-level features, we propose interpolation features to give customers more flexibility. In this paper, we focus on the implementation of such interpolation features into Navigator and its user interface. To assess our approach, we designed and performed user experiments to test and compare Modeler and Navigator, thus providing insights for further developments of our approach. Our results suggest that barycentric interpolation between qualitative parameters provides a more easily understandable interface that empowers novice customers to explore the design space expeditiously.

The use of decision-making models in the early stages of the development of complex products and technologies is a well-established practice in industry. Engineers rely on well-established statistical and mathematical models to explore the feasible design space and make early decisions on future design configurations. At the same time, researchers in both value-driven design and sustainable product development areas have stressed the need to expand the design space exploration by encompassing value and sustainability-related considerations. A portfolio of methods and tools for decision support regarding value and sustainability integration has been proposed in literature, but very few have seen an integration in engineering practices. This paper proposes an approach, developed and tested in collaboration with an aerospace subsystem manufacturer, featuring the integration of value-driven design and sustainable product development models in the established practices for design space exploration. The proposed approach uses early simulation results as input for value and sustainability models, automatically computing value and sustainability criteria as an integral part of the design space exploration. Machine learning is applied to deal with the different levels of granularity and maturity of information among early simulations, value models, and sustainability models, as well as for the creation of reliable surrogate models for multidimensional design analysis. The paper describes the logic and rationale of the proposed approach and its application to the case of a turbine rear structure for commercial aircraft engines. Finally, the paper discusses the challenges of the approach implementation and highlights relevant research directions across the value-driven design, sustainable product development, and machine learning research fields.

One problem in incremental product development is that geometric models are limited in their ability to explore radical alternative design variants. In this publication, a function modeling approach is suggested to increase the amount and variety of explored alternatives, since function models (FM) provide greater model flexibility. An enhanced function-means (EF-M) model capable of representing the constraints of the design space as well as alternative designs is created through a reverse engineering process. This model is then used as a basis for the development of a new product variant. This work describes the EF-M model's capabilities for representing the design space and integrating novel solutions into the existing product structure and explains how these capabilities support the exploration of alternative design variants. First-order analyses are executed, and the EF-M model is used to capture and represent already existing design information for further analyses. Based on these findings, a design space exploration approach is developed. It positions the FM as a connection between legacy and novel designs and, through this, allows for the exploration of more diverse product concepts. This approach is based on three steps – decomposition, design, and embodiment – and builds on the capabilities of EF-M to model alternative solutions for different requirements. While the embodiment step of creating the novel product's geometry is still a topic for future research, the design space exploration concept can be used to enable wider, more methodological, and potentially automated design space exploration.

To be useful for architects and related designers searching for creative, expressive forms, performance-based digital tools must generate a diverse range of design solutions. This gives the designer flexibility to choose from a number of high-performing designs based on aesthetic preferences or other priorities. However, there is no single established method for measuring diversity in the context of computational design, especially in the field of architecture. This paper explores different metrics for quantifying diversity in parametric design, which is an increasingly common digital approach to early-stage exploration, and tests how human users perceive these diversity measurements. It first provides a review of existing methodologies for measuring diversity and describes how they can be adapted for parametrically formulated design spaces. This paper then tests how these different metrics align with human perception of design diversity through an online visual survey. Finally, it offers a quantitative comparison between the different methods and a discussion of their attributes and potential applications. In general, the comparison indicates that at the level of diversity difference that becomes visually meaningful to humans, the measurable difference between metrics is small. This paper informs future researchers, developers, and designers about the measurement of diversity in parametric design, and can stimulate further studies into the perception of diversity within sets of design options, as well as new design methodologies that combine architectural novelty and performance.

The concept of shape grammars has often been proposed to improve or support creative design processes. Shape grammar implementations have the potential to both automate parts of the design process and allow exploration of design alternatives. In many of the existing implementations, the main focus is either on capturing the rationale of a particular existing grammar or on allowing designers to develop a new grammar. However, little attention is typically given to the actual representation of the design space that can be explored in the interface of the implementation. With such representation, a shape grammar implementation could properly support designers who are still in the process of designing and may not yet have a clear shape grammar in mind. In this article, an approach and a proof-of-concept software system is proposed for a shape grammar implementation that provides a visual and interactive way to support design space exploration in a creative design process. We describe the method by which this software system can be used and focus on how designers can interact with the exploration process. In particular, we point out how the proposed approach realizes several important amplification strategies to support design space exploration.

Climate change, resource depletion, and worldwide urbanization feed the demand for more energy and resource-efficient buildings. Increasingly, architectural designers and consultants analyze building designs with easy-to-use simulation tools. To identify design alternatives with good performance, designers often turn to optimization methods. Randomized, metaheuristic methods such as genetic algorithms are popular in the architectural design field. However, are metaheuristics the best approach for architectural design problems that often are complex and ill defined? Metaheuristics may find solutions for well-defined problems, but they do not contribute to a better understanding of a complex design problem. This paper proposes surrogate-based optimization as a method that promotes understanding of the design problem. The surrogate method interpolates a mathematical model from data that relate design parameters to performance criteria. Designers can interact with this model to explore the approximate impact of changing design variables. We apply the radial basis function method, a specific type of surrogate model, to two architectural daylight optimization problems. These case studies, along with results from computational experiments, serve to discuss several advantages of surrogate models. First, surrogate models not only propose good solutions but also allow designers to address issues outside of the formulation of the optimization problem. Instead of accepting a solution presented by the optimization process, designers can improve their understanding of the design problem by interacting with the model. Second, a related advantage is that designers can quickly construct surrogate models from existing simulation results and other knowledge they might possess about the design problem. Designers can thus explore the impact of different evaluation criteria by constructing several models from the same set of data. They also can create models from approximate data and later refine them with more precise simulations. Third, surrogate-based methods typically find global optima orders of magnitude faster than genetic algorithms, especially when the evaluation of design variants requires time-intensive simulations.

The act of design is a complex of actions and abilities that is evolving and often highly individual. Given the context of human–computer interaction, and a commitment to the model of design space exploration, we identify two axes that help position efforts to realize this model: the spectrum of strengths and needs that stretches from the machine to the human, and the time scale of events in design. Considering a section of each reveals a landscape that prefers certain activities and gives rise to particular emphases. This paper places the other authors in this Special Issue upon this map, and argues the value of typed feature structures and information orderings to the endeavor of realizing design space explorers.

Design space exploration is a long-standing focus in computational design research. Its three main threads are accounts of designer action, development of strategies for amplification of designer action in exploration, and discovery of computational structures to support exploration. Chief among such structures is the design space, which is the network structure of related designs that are visited in an exploration process. There is relatively little research on design spaces to date. This paper sketches a partial account of the structure of both design spaces and research to develop them. It focuses largely on the implications of designers acting as explorers.