Design Exploration Process Research Articles

Enhancing box-wing design efficiency through machine learning based optimization

The optimization of wings typically relies on computationally intensive high-fidelity simulations, which restrict the quick exploration of design spaces. To address this problem, this paper introduces a methodology dedicated to optimizing box wing configurations using low-fidelity data driven machine learning approach. This technique showcases its practicality through the utilization of a tailored low-fidelity machine learning technique, specifically designed for early-stage wing configuration. By employing surrogate model trained on small dataset derived from low-fidelity simulations, our method aims to predict outputs within an acceptable range. This strategy significantly mitigates computational costs and expedites the design exploration process. The methodology’s validation relies on its successful application in optimizing the box wing of PARSIFAL, serving as a benchmark, while the primary focus remains on optimizing the newly designed box wing by Bionica. Applying this method to the Bionica configuration led to a notable 14% improvement in overall aerodynamic efficiency. Furthermore, all the optimized results obtained from machine learning model undergo rigorous assessments through the high-fidelity RANS analysis for confirmation. This methodology introduces innovative approach that aims to streamline computational processes, potentially reducing the time and resources required compared to traditional optimization methods.

Surface Design Exploration to Diversify Ciwaringin Cirebon Batik Design

Diversification has been recognized as effective in developing commercial enterprises. However, this strategy requires the commercial business to deeply understand its basic competencies and be aware of changing market trends. In the efforts to develop Ciwaringin batik SMEs, the diversification plan necessitates the Ciwaringin batik SMEs revisiting their unique competencies, namely the hand-writing batik process and the popular trend in 2022/2033, particularly the rising consumption of home décor and hypernature themes. As a result, implementing a diversification strategy in Ciwaringin batik SMEs requires investigating opportunities for developing Ciwaringin batik designs that are unique in their written batik techniques through surface design exploration in order to be responsive to the hypernature-themed home décor trend. Exploration of surface design on textiles was carried out to develop the appearance of Ciwaringin hand-writting batik so that it is relevant to the changing home decor market trends, namely the organic and textured appearances of the hypernature theme. The surface design exploration process is equipped with a data analysis to implement appropriate surface design exploration that supports the Ciwaringin batik diversification strategy. The creative method that combines data analysis and surface design exploration produced several Ciwaringin hand-writing batik designs that display the quality of the organic textured textile surfaces with bright colors typical of the hypernature theme. The Ciwaringin batik designs from surface design exploration are also designed to be home décor items, namely aesthetic wall elements that beautify a residential space.

A performance-centric ML-based multi-application mapping technique for regular Network-on-Chip

This research article discusses the challenges faced by the Network-on-Chip (NoC) architecture due to increased integration density and proposes a novel fault-tolerant multi-application mapping approach called ”FANC.” The approach is based on Machine Learning (ML) and can provide solutions for unseen graphs and topologies without prior training. The proposed technique uses an ML-based model to extract relevant information from the search data and incorporate it into the search process. This results in a more robust model with a higher convergence rate and solution quality. The approach is evaluated using a variety of simulation parameters, such as communication cost, network latency, throughput, and power usage. Static simulations are performed in a Python programming environment, while dynamic simulations are performed with a SystemC-based cycle-accurate NoC simulator and the Orion2.0 Power tool. The results show that FANC reduces communication costs by 266%. It also improves network latency by 9%, throughput by 1%, and power consumption by 7%. The approach also simplifies and minimizes the search area in the design exploration process and can be used as an auxiliary component for other optimization algorithms.

QUIDAM: A Framework for Qu ant i zation-aware D NN A ccelerator and M odel Co-Exploration

As the machine learning and systems communities strive to achieve higher energy efficiency through custom deep neural network (DNN) accelerators, varied precision or quantization levels, and model compression techniques, there is a need for design space exploration frameworks that incorporate quantization-aware processing elements into the accelerator design space while having accurate and fast power, performance, and area models. In this work, we present QUIDAM , a highly parameterized quantization-aware DNN accelerator and model co-exploration framework. Our framework can facilitate future research on design space exploration of DNN accelerators for various design choices such as bit precision, processing element type, scratchpad sizes of processing elements, global buffer size, number of total processing elements, and DNN configurations. Our results show that different bit precisions and processing element types lead to significant differences in terms of performance per area and energy. Specifically, our framework identifies a wide range of design points where performance per area and energy varies more than 5× and 35×, respectively. With the proposed framework, we show that lightweight processing elements achieve on par accuracy results and up to 5.7× more performance per area and energy improvement when compared to the best 16-bit integer quantization–based implementation. Finally, due to the efficiency of the pre-characterized power, performance, and area models, QUIDAM can speed up the design exploration process by three to four orders of magnitude as it removes the need for expensive synthesis and characterization of each design.

Design Explorations for 3D-Printed Modular Markers for eXtended-Reality Tangible User Interfaces

Various materials, objects, and sensors have been explored earlier for creating tangible user interfaces (TUIs). However, there is little work on 3D-printed TUIs based on visual markers for smartphone-based extended reality (XR) experiences. The combination of visual markers and smartphones results in cheap, accessible XR systems within reach of many people. Combined with 3D printing, it could foster do-it-yourself (DIY) projects for XR experiences, which may further expand and open-up possibilities for accessible and tangible interaction. This work explores the design space of modular 3D-printed tangibles for smartphone-based XR. The authors report the design exploration process, provide several interactive 3D-printed markers, and reflect on the resulting possibilities.

CreativeSearch: Proactive design exploration system with Bayesian information gain and information entropy

A spatial designer's search goal is sequentially updated during the spatial design exploration process. The design exploration process requires a proactive process that supports better-informed design decisions and refreshes the search direction to avoid being fixed; however, no study has been conducted to date. This paper describes a framework called CreativeSearch, which provides three types of guidance feedback by analyzing a spatial designer's design process. Particularly, by integrating the Bayesian Information Gain (BIG) framework and information entropy theory, CreativeSearch provides three types of guidance feedback. A user study conducted with 20 spatial designers confirmed that CreativeSearch supported them in exploring creative design ideas by lowering their mental load. Furthermore, CreativeSearch made designers proactive and supported their design decisions. The guidance feedback framework described in this paper is expected to extend the research on proactive and creative design exploration processes.

Design exploration by using a genetic algorithm and the Theory of Inventive Problem Solving (TRIZ)

This paper presents a computational design exploration method called GA+TRIZ, which aids designers in defining the design problem clearly, making a parametric model where pertinent variables are included, obtaining a series of suitable solutions, and resolving existing conflicts among design objectives. The goal is to include the designer's qualitative and performance-based quantitative design goals in the design process, while promoting innovative ideas for resolving contradictory design objectives. The method employed is a Genetic Algorithm (GA), earlier implemented in an automated design exploration process called ParaGen, in combination with the Theory of Inventive Problem Solving (TRIZ), a novel methodology to assist architects and structural engineers in the conceptual phase of design. The GA+TRIZ method promotes automated design exploration, investigation of unexpected solutions, and continuous interaction with the computational generating system. Finally, this paper presents two examples that illustrate how the GA+TRIZ method assists designers in problem structuring, design exploration, and decision-making.

Designer-Centric Spatial Design Support

Design-centric systems focus on searching or generating design alternatives using design retrieval or generation algorithms. Conversely, designer-centric systems focus on providing design feedback to refine design ideas through interactions with designers when creating design alternatives. This paper proposes a novel designer-centric design support system that analyzes user design moves and provides real-time feedback to augment the spatial design exploration process. Specifically, we developed a system that integrated sketching, design retrieval, and feedback features for tablet PCs. The user experiments involving 21 professional spatial designers verified that the proposed system helped users interact with their designs, navigate novel design spaces, reevaluate design moves, and embody design ideas. These findings provided implications for developing an interactive design exploration method for coordinating designers and computers, thereby extending design support system research. Further, we discuss applications and challenges in implementing the system on the web, revealing possibilities for data trailing of design moves.

Investigations on motorbike frame material and comparative analysis using generative design and topology optimization

The frame is one of the most important load-bearing structures of a motorbike. Since various components are mounted on the frame, hence rigorous analysis is required based on different design techniques. Among the different techniques for design, generative design and topology optimization are used in the present work. Generative design is a design exploration process that uses machine learning and cloud computing and generates a large number of designs starting from restricted design data. The generative design domain includes geometric feasibility, manufacturability, and cost analysis, which provides the opportunity to achieve design objectives for optimized models. Topology optimization is a mathematical methodology that re-arranges the material distribution within the design domain, for different loads, boundary conditions, and constraints on functional and optimized models. A comparative study based on the mechanical properties of two different designs of motorbike frames created using generative design and topology optimization is proposed. Both of these processes are aimed towards light-weighting the structural models to maximize the performance of the intended design. The practical use of the generative design and topology optimization methodology is demonstrated using a generic CAD-based design of a trellis frame structure and the generative workspace in Autodesk FUSION 360. Comparative analysis was carried out between the conventional Trellis frame, Generative Frame and Topology Optimized frame structures. Distribution of induced stresses within the frame, nodal displacement and equivalent strain are investigated in the present work. Structural mass is decreased by 26.63% in the case of topology optimization and 34.29 % for generative design.

A Benchmark Model for Predicting Building Energy and Daylight Performance in The Early Phase of Design Utilizing Parametric Design Exploration

Along with the enormous impact on computational development in architecture and urban design, the way in approaching the built environment is shifting and intended to look closer to performance and evidence-based design. This development holds promise in handling complex computation to approach desired targeted design goals. However, the implementation of form-finding and design performance optimization still lacks, particularly in Japan’s sub-tropical climate. This paper describes the parametric design and design exploration process’s implementation through the generative algorithm platform to develop a benchmark model to predict building energy and daylight performance and find possible design solutions from the iteration process during the early phase of the design process. The variables incorporated related to the glazing ratio, the length of the overhang, and building orientation. Grasshopper, a parametric-based plugin that works in Rhinoceros, is used to arrange a parametric definition for the overall experiment. The tools used to investigate the environmental analysis and energy consumption are Ladybug and Honeybee, and the exploration process will be conducted using Design Explorer. The context will be situated in Orio district and uses the EPW file of Kitakyushu city, Fukuoka, Japan. The results of this research furthermore can potentially be a comparison for more dynamic factors.

A machine learning framework for accelerating the design process using CAE simulations: An application to finite element analysis in structural crashworthiness

This paper presents a novel framework for predicting computer-aided engineering (CAE) simulation results using machine learning (ML). The framework is applied to finite element (FE) simulations of dynamic axial crushing of rectangular crush tubes that are typically used in vehicle crashworthiness applications. A virtual design of experiments that varies the size and wall thickness of the FE model is performed to generate the necessary training data. This process generates designs with varying numbers of nodes and elements that are handled by the ML system. However, the explicit design parameters and meshing techniques that were used to generate the training data remain unknown to the ML system. Instead, 3D convolutional neural networks (CNN) autoencoders are used to process the initial FE model data (i.e., nodes, elements, thickness, etc.) to automatically determine these features in an unsupervised manner. A voxelization strategy that operates on the mass of individual nodes is proposed to handle the unstructured nature of the nodes and elements while capturing variations in the wall thickness of the FE models. The flattened latent space generated by the 3D-CNN-autoencoder is then used as input into long-short term memory neural networks (LSTM-NN) to predict the force–displacement response as well as the deformation of the mesh. The training process of both the 3D-CNN-autoencoders and LSTM-NN is systematically studied to highlight the robustness of the framework. The proposed ML system utilizes only 16% of the simulations generated in the virtual design of experiments to achieve good predictive capability. Once trained, the proposed framework can predict the deformation of the mesh and resulting force–displacement response of a new design up to ∼330 and ∼2,960,000 times faster, respectively, than the conventional FE approach with good accuracy. This computational speed up offers design engineers and scientists a potential tool for accelerating the design exploration process with CAE simulation tools, such as FE analysis.

Automatic Selection of Structure Parameters of Silicon on Insulator Lateral Power Device Using Bayesian Optimization

The selection of design structure parameters is a critical step for meeting the performance requirement for silicon on insulator (SOI) lateral power devices, especially when multiple design constraints are presented. In this letter, we propose a fully-automated structural design method for SOI lateral power device based on a Bayesian optimization (BO) framework. Given the design target characterized by breakdown voltage (BV) and on-resistance (Ron) specifications, the proposed approach searches for the optimal structure that can satisfy the constraints. The experimental results demonstrate that designs obtained from our optimization framework fall within 5% range from the desired specifications when evaluated using technology computer-aided design (TCAD) simulation, which proves the efficiency of the proposed approach. Besides, the efficiency of our proposed approach is reflected by its runtime which does not exceed 10 minutes for more than 70% of the cases. We believe that this modeling method can greatly accelerate the design exploration process of power devices for designers.

Design optimisation using convex programming: Towards waste-efficient building designs

A non-modular building layout is amongst the leading sources of offcut waste, resulting from a substantial amount of onsite cutting and fitting of bricks, blocks, plasterboard, and tiles. The field of design for dimensional coordination is concerned with finding an optimal configuration for non-overlapping spaces in the layout to reduce materials waste. In this article, we propose a convex optimisation-based algorithm for finding alternative floor layouts to enforce the design for dimensional coordination. At the crux of the proposed algorithm lies two mathematical models. The first is the convex relaxation model that establishes the topology of spaces within the layout through relative positioning constraints. We employed acyclic graphs to generate a minimal set of relative positioning constraints to model the problem. The second model optimises the geometry of spaces based on the modular size. The algorithm exploits aspect ratio constraints to restrict the generation of alternate layouts with huge variations. The algorithm is implemented in the BIMWaste tool for automating the design exploration process. BIMWaste is capable of investigating the degree to which designers consider dimensional coordination. We tested the algorithm over 10 completed building projects to report its suitability and accuracy. The algorithm generates competitive floor layouts for the same client intent that are likely to be tidier and more modular. More importantly, those floor layouts have improved waste performance (i.e., 8.75% less waste) due to a reduced tendency for material cutting and fitting. This study, for the first time, used convex programming for the design optimisation with a focus to reduce construction waste.

Design Exploration of Reliably Manufacturable Materials and Structures With Applications to Negative Stiffness Metamaterials and Microstereolithography1

One of the challenges in designing metamaterials for additive manufacturing (AM) is accounting for the differences between as-designed and as-built geometries and material properties. From a designer's perspective, these differences can lead to degradation of part and metamaterial performance, which is especially difficult to accommodate in small-lot or one-of-a-kind production. In this context, each part is unique, and therefore, extensive iteration is costly. Designers need a means of exploring the design space while simultaneously considering the reliability of additively manufacturing particular candidate designs. In this work, a design exploration approach, based on Bayesian network classifiers (BNC), is extended to incorporate manufacturing variation into the design exploration process and identify designs that reliably meet performance requirements when this variation is taken into account. The example application is the design of negative stiffness (NS) metamaterials, in which small volume fractions of NS inclusions are embedded within a host material. The resulting metamaterial or composite exhibits macroscopic mechanical stiffness and loss properties that exceed those of the base matrix material. The inclusions are fabricated with microstereolithography with features on the scale of tens of microns, but variability is observed in material properties and dimensions from specimen to specimen. This variability is measured and modeled via design, fabrication, and characterization of metrology parts. The quantified manufacturing variability is incorporated into the BNC approach as a manufacturability classifier to identify candidate designs that achieve performance targets reliably, even when manufacturing variability is taken into account.

Evaluation of go-kart aerodynamic efficiency using CFD, RBF mesh morphing and lap time simulation

The study featured in this paper presents a CFD, RBF mesh morphing and lap time simulation-based analysis method to investigate the effect of aerodynamic drag on go-kart performance. A bodywork design exploration is performed, employing an automatic response surface optimisation, to evaluate effects of its shape on aerodynamic penetration. The effect of driver size is explored as well by adapting the mesh according to a parametric mannequin positioning workflow. Results of the design exploration process highlight a very good optimisation of the baseline configuration with respect to aerodynamic efficiency. On the other hand, driver size reduction produces a noticeable drag decrease in the order of 4.5%. To translate registered drag changes into performance variations, an in-house lap time simulator based on a lumped parameter model is adopted. Lap time for two different karting circuits are simulated in high and low drag configurations resulting in lap time variations in the order of 0.1/0.2 sec.

Performance Assessment Strategies

Using engineering performance evaluations to explore design alternatives during the conceptual phase of architectural design helps to understand the relationships between form and performance; and is crucial for developing well-performing final designs. Computer aided conceptual design has the potential to aid the design team in discovering and highlighting these relationships; especially by means of procedural and parametric geometry to support the generation of geometric design, and building performance simulation tools to support performance assessments. However, current tools and methods for computer aided conceptual design in architecture do not explicitly reveal nor allow for backtracking the relationships between performance and geometry of the design. They currently support post-engineering, rather than the early design decisions and the design exploration process. Focusing on large roofs, this research aims at developing a computational design approach to support designers in performance driven explorations. The approach is meant to facilitate the multidisciplinary integration and the learning process of the designer; and not to constrain the process in precompiled procedures or in hard engineering formulations, nor to automatize it by delegating the design creativity to computational procedures. PAS (Performance Assessment Strategies) as a method is the main output of the research. It consists of a framework including guidelines and an extensible library of procedures for parametric modelling. It is structured on three parts. Pre-PAS provides guidelines for a design strategy-definition, toward the parameterization process. Model-PAS provides guidelines, procedures and scripts for building the parametric models. Explore-PAS supports the solutions-assessment based on numeric evaluations and performance simulations, until the identification of a suitable design solution. PAS has been developed based on action research. Several case studies have focused on each step of PAS and on their interrelationships. The relations between the knowledge available in pre-PAS and the challenges of the solution space exploration in explore-PAS have been highlighted. In order to facilitate the explore-PAS phase in case of large solution spaces, the support of genetic algorithms has been investigated and the exiting method ParaGen has been further implemented. Final case studies have focused on the potentials of ParaGen to identify well performing solutions; to extract knowledge during explore-PAS; and to allow interventions of the designer as an alternative to generations driven solely by coded criteria. Both the use of PAS and its recommended future developments are addressed in the thesis.

Performance Assessment Strategies: A computational framework for conceptual design of large roofs

Using engineering performance evaluations to explore design alternatives during the conceptual phase of architectural design helps to understand the relationships between form and performance; and is crucial for developing well-performing final designs. Computer aided conceptual design has the potential to aid the design team in discovering and highlighting these relationships; especially by means of procedural and parametric geometry to support the generation of geometric design, and building performance simulation tools to support performance assessments. However, current tools and methods for computer aided conceptual design in architecture do not explicitly reveal nor allow for backtracking the relationships between performance and geometry of the design. They currently support post-engineering, rather than the early design decisions and the design exploration process. Focusing on large roofs, this research aims at developing a computational design approach to support designers in performance driven explorations. The approach is meant to facilitate the multidisciplinary integration and the learning process of the designer; and not to constrain the process in precompiled procedures or in hard engineering formulations, nor to automatize it by delegating the design creativity to computational procedures. PAS (Performance Assessment Strategies) as a method is the main output of the research. It consists of a framework including guidelines and an extensible library of procedures for parametric modelling. It is structured on three parts. Pre-PAS provides guidelines for a design strategy-definition, toward the parameterization process. Model-PAS provides guidelines, procedures and scripts for building the parametric models. Explore-PAS supports the solutions-assessment based on numeric evaluations and performance simulations, until the identification of a suitable design solution. PAS has been developed based on action research. Several case studies have focused on each step of PAS and on their interrelationships. The relations between the knowledge available in pre-PAS and the challenges of the solution space exploration in explore-PAS have been highlighted. In order to facilitate the explore-PAS phase in case of large solution spaces, the support of genetic algorithms has been investigated and the exiting method ParaGen has been further implemented. Final case studies have focused on the potentials of ParaGen to identify well performing solutions; to extract knowledge during explore-PAS; and to allow interventions of the designer as an alternative to generations driven solely by coded criteria. Both the use of PAS and its recommended future developments are addressed in the thesis.

Performance Assessment Strategies

Using engineering performance evaluations to explore design alternatives during the conceptual phase of architectural design helps to understand the relationships between form and performance; and is crucial for developing well-performing final designs. Computer aided conceptual design has the potential to aid the design team in discovering and highlighting these relationships; especially by means of procedural and parametric geometry to support the generation of geometric design, and building performance simulation tools to support performance assessments. However, current tools and methods for computer aided conceptual design in architecture do not explicitly reveal nor allow for backtracking the relationships between performance and geometry of the design. They currently support post-engineering, rather than the early design decisions and the design exploration process. Focusing on large roofs, this research aims at developing a computational design approach to support designers in performance driven explorations. The approach is meant to facilitate the multidisciplinary integration and the learning process of the designer; and not to constrain the process in precompiled procedures or in hard engineering formulations, nor to automatize it by delegating the design creativity to computational procedures. PAS (Performance Assessment Strategies) as a method is the main output of the research. It consists of a framework including guidelines and an extensible library of procedures for parametric modelling. It is structured on three parts. Pre-PAS provides guidelines for a design strategy-definition, toward the parameterization process. Model-PAS provides guidelines, procedures and scripts for building the parametric models. Explore-PAS supports the solutions-assessment based on numeric evaluations and performance simulations, until the identification of a suitable design solution. PAS has been developed based on action research. Several case studies have focused on each step of PAS and on their interrelationships. The relations between the knowledge available in pre-PAS and the challenges of the solution space exploration in explore-PAS have been highlighted. In order to facilitate the explore-PAS phase in case of large solution spaces, the support of genetic algorithms has been investigated and the exiting method ParaGen has been further implemented. Final case studies have focused on the potentials of ParaGen to identify well performing solutions; to extract knowledge during explore-PAS; and to allow interventions of the designer as an alternative to generations driven solely by coded criteria. Both the use of PAS and its recommended future developments are addressed in the thesis.

Performance Assessment Strategies: A computational framework for conceptual design of large roofs

Using engineering performance evaluations to explore design alternatives during the conceptual phase of architectural design helps to understand the relationships between form and performance; and is crucial for developing well-performing final designs. Computer aided conceptual design has the potential to aid the design team in discovering and highlighting these relationships; especially by means of procedural and parametric geometry to support the generation of geometric design, and building performance simulation tools to support performance assessments. However, current tools and methods for computer aided conceptual design in architecture do not explicitly reveal nor allow for backtracking the relationships between performance and geometry of the design. They currently support post-engineering, rather than the early design decisions and the design exploration process. Focusing on large roofs, this research aims at developing a computational design approach to support designers in performance driven explorations. The approach is meant to facilitate the multidisciplinary integration and the learning process of the designer; and not to constrain the process in precompiled procedures or in hard engineering formulations, nor to automatize it by delegating the design creativity to computational procedures. PAS (Performance Assessment Strategies) as a method is the main output of the research. It consists of a framework including guidelines and an extensible library of procedures for parametric modelling. It is structured on three parts. Pre-PAS provides guidelines for a design strategy-definition, toward the parameterization process. Model-PAS provides guidelines, procedures and scripts for building the parametric models. Explore-PAS supports the solutions-assessment based on numeric evaluations and performance simulations, until the identification of a suitable design solution. PAS has been developed based on action research. Several case studies have focused on each step of PAS and on their interrelationships. The relations between the knowledge available in pre-PAS and the challenges of the solution space exploration in explore-PAS have been highlighted. In order to facilitate the explore-PAS phase in case of large solution spaces, the support of genetic algorithms has been investigated and the exiting method ParaGen has been further implemented. Final case studies have focused on the potentials of ParaGen to identify well performing solutions; to extract knowledge during explore-PAS; and to allow interventions of the designer as an alternative to generations driven solely by coded criteria. Both the use of PAS and its recommended future developments are addressed in the thesis.

Performance Assessment Strategies: A computational framework for conceptual design of large roofs

Using engineering performance evaluations to explore design alternatives during the conceptual phase of architectural design helps to understand the relationships between form and performance; and is crucial for developing well-performing final designs. Computer aided conceptual design has the potential to aid the design team in discovering and highlighting these relationships; especially by means of procedural and parametric geometry to support the generation of geometric design, and building performance simulation tools to support performance assessments. However, current tools and methods for computer aided conceptual design in architecture do not explicitly reveal nor allow for backtracking the relationships between performance and geometry of the design. They currently support post-engineering, rather than the early design decisions and the design exploration process. Focusing on large roofs, this research aims at developing a computational design approach to support designers in performance driven explorations. The approach is meant to facilitate the multidisciplinary integration and the learning process of the designer; and not to constrain the process in precompiled procedures or in hard engineering formulations, nor to automatize it by delegating the design creativity to computational procedures. PAS (Performance Assessment Strategies) as a method is the main output of the research. It consists of a framework including guidelines and an extensible library of procedures for parametric modelling. It is structured on three parts. Pre-PAS provides guidelines for a design strategy-definition, toward the parameterization process. Model-PAS provides guidelines, procedures and scripts for building the parametric models. Explore-PAS supports the solutions-assessment based on numeric evaluations and performance simulations, until the identification of a suitable design solution. PAS has been developed based on action research. Several case studies have focused on each step of PAS and on their interrelationships. The relations between the knowledge available in pre-PAS and the challenges of the solution space exploration in explore-PAS have been highlighted. In order to facilitate the explore-PAS phase in case of large solution spaces, the support of genetic algorithms has been investigated and the exiting method ParaGen has been further implemented. Final case studies have focused on the potentials of ParaGen to identify well performing solutions; to extract knowledge during explore-PAS; and to allow interventions of the designer as an alternative to generations driven solely by coded criteria. Both the use of PAS and its recommended future developments are addressed in the thesis.