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The study integrated understanding by design-Internet of Things (UbD-IoT) education with design thinking and computational thinking to plan and design an IoT course. Cross-domain application examples were employed to train students in problem-understanding, deep thinking and logical design for IoT applications.

In this study, the UbD model was integrated with design thinking and computational thinking in the planning and design of an IoT course. The examples of cross-domain applications were used to train students to understand a problem by engaging themselves in deep thinking and helping them think and design logically for an IoT application.

The UbD-IoT learning design greatly decreased students' overall cognitive load. UbD-IoT learning has a significant impact on the performance of computational thinking in problem-solving and problem-understanding. The impact of UbD-IoT learning on logical thinking and program learning cognition in students needs to be verified.

The results of this study have shown that the UbD model is effective in reducing the cognitive load of a learning course and also strengthens T-competencies in the lateral skills of computational thinking, critical problem-solving, logical thinking and creative thinking.

The purpose of this paper is to describe a novel design method to construct lattice structure computational models composed of a set of unit cells including simple cubic, body-centered cubic, face-centered cubic, diamond cubic and octet cubic unit cell.

In this paper, the authors introduce a new implicit design algorithm based on the computation of volumetric distance field (VDF). All the geometric components including lattice core structure and outer skin are represented with VDFs in a given design domain. This enables computationally efficient design of a computational model for an arbitrarily complex lattice structure. In addition, the authors propose a hybrid method based on the VDF and parametric solid models to construct a conformal lattice structure, which is oriented in accordance with the geometric form of the exterior surface. This method enables the authors to design highly complex lattice structure, computational models, in a consistent design framework irrespective of the complexity in geometric representations without sacrificing accuracy and efficiency.

Experimental results are shown for a variety of geometries to validate the proposed design method along with illustrative several lattice structure prototypes built by additive manufacturing techniques.

This method enables the authors to design highly complex lattice structure, computational models, in a consistent design framework irrespective of the complexity in geometric representations without sacrificing accuracy and efficiency.

The purpose of this study was to create a corset—understructure as well as fabric covering—using only computational, 3D approaches to fashion design. The process incorporated 3D body scan data, parametric methods for the 3D-printed design, and algorithmic methods for the automated, custom-fit fabric pattern.

The methods or protocol-based framework that nucleated this design project (see Figure 1) enabled more concentrated research into the iterative step-by-step procedure and the computational techniques used herein.

The 3D computational methods in this study demonstrated a new way of rendering the body-to-pattern relationship through the use of multiple software platforms. Using body scan data and computer coding, the computational construction methods in this study showed a pliant and sustainable method of clothing design where designers were able to manipulate the X, Y, and Z coordinates of the points on the scan surface.

A study of algorithmic methods is inherently a study of limitation. The iterative process of design was defined and refined through the particularity of an algorithm, which required thoughtful manipulation to inform the outcome of this research.

This study sought to illustrate the use and limitations of algorithm-driven computer programming to advance creative design practices.

As body scan data and biometric information become increasingly common components of computational fashion design practices, the need for more research on the use of these techniques is pressing. Moreover, computational techniques serve as a catalyst for discussions about the use of biometric information in design and data modeling.

The process of designing in 3D allowed for the dynamic capability to manipulate proportion and form using parametric design techniques.

This paper aims to present a multi-fidelity surrogate-based optimization (MFSBO) method for computationally accurate and efficient design of microfluidic concentration gradient generators (µCGGs).

Cokriging-based multi-fidelity surrogate model (MFSM) is constructed to combine data with varying fidelities and computational costs to accelerate the optimization process and improve design accuracy. An adaptive sampling approach based on parallel infill of multiple low-fidelity (LF) samples without notably adding computation burden is developed. The proposed optimization framework is compared with a surrogate-based optimization (SBO) method that relies on data from a single source, and a conventional multi-fidelity adaptive sampling and optimization method in terms of the convergence rate and design accuracy.

The results demonstrate that proposed MFSBO method allows faster convergence and better designs than SBO for all case studies with 49% more reduction in the objective function value on average. It is also found that parallel infill (MFSBO-4) with four LF samples, enables more robust, efficient and accurate designs than conventional multi-fidelity infill (MFSBO-1) that only adopts one LF sample during each iteration for more complex optimization problems.

A MFSM based on cokriging method is constructed to utilize data with varying fidelities, accuracies and computational costs for µCGG design. A parallel infill strategy based on multiple infill criteria is developed to accelerate the convergence and improve the design accuracy of optimization. The proposed methodology is proved to be a feasible method for µCGG design and its computational efficiency is verified.

The purpose of this paper is to find optimal solutions for conceptual design automation, which can be integrated with Building Information Modelling (BIM) support for construction automation. Problems relating ostensibly to failures in computational support for the conceptual design stage are well-documented in extant literature. These failures are multifarious and significant, with several deficiencies being acknowledged in the Architecture, Engineering, and Construction (AEC) industry. Whilst acknowledging this, extant literature has highlighted the importance of computational design in the AEC industry; and failures in this area include the need to strengthen the congruent links and support mechanisms in order to exploit the opportunities presented by new computational design methods. Given this, it is postulated that the application of generative design could enhance the design experience by assisting designers with the iterative generation of alternatives and parameterisation (change management) processes. Moreover, as BIM applications are increasingly providing comprehensive support for modelling and management, then additional synergies could be examined for further exploitation.

This paper focusses on the potential for developing an interactive BIM environment that purposefully adopts generative design as a method of computational design for the early design stages. This research facilitates the automation of the conceptual architectural design process, using BIM as the central conduit for enhancing the integration of the whole building design process (including design interfaces). This approach is designed to improve designers’ cognition and collaboration during the conceptual architectural design process.

This paper evaluates the existing methods and decision support mechanisms, and it introduces the potential of combining different concepts into a single environment (generative design/BIM).

This research is novel, in that it critically appraises virtual generative workspaces using BIM as the central conduit. The outcome and intervention of this research forms a theoretical basis for the development of a “proof of concept” prototype, which actively engages generative design into a single dynamic BIM environment to support the early conceptual design process.

The purpose of this paper is to conduct a comparative analysis between a straight blade (SB) and a curved caudal-fin tidal turbine blade (CB) and to examine the aspects relating to geometry, turbulence modelling, non-dimensional forces lift and power coefficients.

The comparison utilises results obtained from a default horizontal axis tidal turbine with turbine models available from the literature. A computational design method was then developed and implemented for “horizontal axis tidal turbine blade”. Computational fluid dynamics (CFD) results for the blade design are presented in terms of lift coefficient distribution at mid-height blades, power coefficients and blade surface pressure distributions. Moving the CB back towards the SB ensures that the total blade height stays constant for all geometries. A 3D mesh independency study of a “straight blade horizontal axis tidal turbine blade” modelled using CFD was carried out. The grid convergence study was produced by employing two turbulence models, the standard k-ε model and shear stress transport (SST) in ANSYS CFX. Three parameters were investigated: mesh resolution, turbulence model, and power coefficient in the initial CFD, analysis.

It was found that the mesh resolution and the turbulence model affect the power coefficient results. The power coefficients obtained from the standard k-ε model are 15 to 20 per cent lower than the accuracy of the SST model. Further analysis was performed on both the designed blades using ANSYS CFX and SST turbulence model. The variation in pressure distributions yields to the varying lift coefficient distribution across blade spans. The lift coefficient reached its peak between 0.75 and 0.8 of the blade span where the total lift accelerates with increasing pressure before drastically dropping down at 0.9 onwards due to the escalating rotational velocity of the blades.

The work presents a computational design methodological approach that is entirely original. While this numerical method has proven to be accurate and robust for many traditional tidal turbines, it has now been verified further for CB tidal turbines.

This study uses computer-aided design to improve the ecological and environmental sustainability of early-stage landscape designs. Urban expansion on open land and natural habitats has led to a decline in biodiversity and increased climate change impacts, affecting urban inhabitants' quality of life and well-being. While sustainability indicators have been employed to assess the performance of buildings and neighbourhoods, landscape designs' ecological and environmental sustainability has received comparatively less attention, particularly in early-design stages where applying sustainability approaches is impactful.

The authors propose a computation framework for evaluating key landscape sustainability indicators and providing real-time feedback to designers. The method integrates spatial indicators with widely recognized sustainability rating system credits. A specialized tool was developed for measuring biomass optimization, precipitation management and urban heat mitigation, and a proof-of-concept experiment tested the tool's effectiveness on three Mediterranean neighbourhood-level designs.

The results show a clear connection between the applied design strategy to the indicator behaviour. This connection enhances the ability to establish sustainability benchmarks for different types of landscape developments using parametric design.

The study allows non-expert designers to measure and embed landscape sustainability early in the design stages, thus lowering the entry level for incorporating biodiversity enhancement and climate mitigation approaches.

This study expands the parametric vocabulary for measuring landscape sustainability by introducing spatial ecosystem services and architectural sustainability indicators on a unified platform, enabling the integration of critical climate and biodiversity-loss solutions earlier in the development process.

Engineering design optimization involving computational simulations is usually a time-consuming, even computationally prohibitive process. To relieve the computational burden, the adaptive metamodel-based design optimization (AMBDO) approaches have been widely used. This paper aims to develop an AMBDO approach, a lower confidence bounding approach based on the coefficient of variation (CV-LCB) approach, to balance the exploration and exploitation objectively for obtaining a global optimum under limited computational budget.

In the proposed CV-LCB approach, the coefficient of variation (CV) of predicted values is introduced to indicate the degree of dispersion of objective function values, while the CV of predicting errors is introduced to represent the accuracy of the established metamodel. Then, a weighted formula, which takes the degree of dispersion and the prediction accuracy into consideration, is defined based on the already-acquired CV information to adaptively update the metamodel during the optimization process.

Ten numerical examples with different degrees of complexity and an AIAA aerodynamic design optimization problem are used to demonstrate the effectiveness of the proposed CV-LCB approach. The comparisons between the proposed approach and four existing approaches regarding the computational efficiency and robustness are made. Results illustrate the merits of the proposed CV-LCB approach in computational efficiency and robustness.

The proposed approach exhibits high efficiency and robustness in engineering design optimization involving computational simulations.

CV-LCB approach can balance the exploration and exploitation objectively.

The purpose of this article is to analyze the optimization process in depth, elaborating on the components of the entire process and the techniques used. Researchers have been drawn to the expanding trend of optimization since the turn of the century. The rate of research can be used to measure the progress and increase of this optimization procedure. This study is phenomenal to understand the optimization process and different algorithms in addition to their application by keeping in mind the current computational power that has increased the implementation for several engineering applications.

Two-dimensional analysis has been carried out for the optimization process and its approaches to addressing optimization problems, i.e. computational power has increased the implementation. The first section focuses on a thorough examination of the optimization process, its objectives and the development of processes. Second, techniques of the optimization process have been evaluated, as well as some new ones that have emerged to overcome the above-mentioned problems.

This paper provided detailed knowledge of optimization, several approaches and their applications in civil engineering, i.e. structural, geotechnical, hydraulic, transportation and many more. This research provided tremendous emerging techniques, where the lack of exploratory studies is to be approached soon.

Optimization processes have been studied for a very long time, in engineering, but the current computational power has increased the implementation for several engineering applications. Besides that, different techniques and their prediction modes often require high computational strength, such parameters can be mitigated with the use of different techniques to reduce computational cost and increase accuracy.

Advances in digital design tools enable exploration and generation of dynamic building facades. However, some processes are formally prescribed and manually driven to only visualize the design concepts. The purpose of this paper is to present a proactive framework for integrating parametric design thinking, paying particular attention to building facade patterning.

This work developed the PatternGen^© add-on in Autodesk® Revit which utilizes an analytical image data (AID) overlay approach as a data source to dynamically pattern the building facade. The add-on was used to manipulate the placement rules of curtain panels on facade surface geometry. As means of validating this research model, a real-life design project has been chosen to illustrate the practical application of this approach. Feedback and observations from a short end-user questionnaire assessed qualitatively the facade patterning and panelization approach.

The proposed merge (or overlay) of AID images can be used as a parametric thinking method rather than just theory to generate and articulate dynamic facade design. The facade panelization responds to an AID that resembles design-performance data (e.g. solar exposure, interior privacy importance and aesthetics).

This work identifies a form of parametric thinking defined as the expression of geometrical relationships and its configuration dependent on the AID pixel Red Green Blue color source values. In this type of thinking, it explores the impact of the digital process and parametric thinking utility when driven by an AID overlay. The framework highlighted the practical application of AID pixel approach within a digital process to benefit both designers and computational tools developer on emerging design innovations.