Real Design Research Articles

Investigating envelope retrofitting potential and resilience of Australian residential buildings − A stock modelling approach

Residential buildings consume around 24 percent of overall electricity use in Australia. Though the energy efficiency of new buildings is progressively increasing, there are around 10 million existing homes most with poor thermal and energy performance. Improving these existing homes are imperative for Australia to reach its target of net zero by 2050. This paper adopts a stock modelling approach using real designs of more than two hundred thousand detached dwellings and apartments submitted for certification in eight states and territories across Australia to investigate envelope retrofitting potential of the housing stock. The performance of the existing housing stock was assessed and the effect of various levels of envelope improvements on energy efficiency and overheating were analysed for current and future climatic conditions. With improvements in 10% of the entire building stock, 3.14 Mt, 3.52 Mt and 7.2 Mt CO2 emission reductions per year respectively were estimated with Rehab, Refurb and Renov improvements. Both detached dwellings and apartments experienced overheating across all the states and territories for the current and future climatic conditions. Though envelope improvement caused some reduction in the overheating, it was not significant. The high average overheating hours in Queensland come from nighttime overheating in the bedrooms, whereas in most other states daytime overheating constitute the majority. Various improvements in the building envelope were effective in reducing daytime overheating reductions rather than nighttime overheating, though the impact was marginal. The results from this study provided guidance on policy and regulatory mechanisms that can be introduced for developing decarbonization pathways for buildings at a national level.

AUTOMATIC PATH PROGRAMMING FOR THE MANUFACTURING OF SHEET METAL COMPONENTS USING INCREMENTAL FORMING TECHNOLOGY

Asymmetric Incremental Sheet Forming (AISF) is an unconventional forming technology that involves localized and progressive plastic deformation of a sheet using a tool, typically a punch, following a continuous and numerically controlled path. Due to its specific characteristics and advantages, such as flexibility and relatively low implementation cost, it is considered a technology with great potential for competitively producing small series of sheet metal components. However, there are still some challenges, such as the lack of specific CAM tools for this technology, which has hindered its industrialization. Although the process may appear simple, with a 3D path sufficient to transform the original flat sheet into the final part, it has been observed that the manufacturing of real designs requires extensive knowledge of the technology to choose the appropriate path based on the target geometry. In this regard, this study presents a new solution developed by Tecnalia that incorporates expert process knowledge and enables automatic programming of Incremental Forming paths. Specifically, the solution allows programming paths aimed at manufacturing different geometric features commonly found in sheet metal components. This development has been validated through the manufacturing of several parts in a robotic cell implemented at Southeast University in Nanjing.

Optimization Analysis of Clothing Fabric Design Based on Autoencoder and Generative Adversarial Neural Network

In order to meet the challenges of increasingly diverse clothing styles and consumer demands in the market, traditional fabric design methods have become difficult to meet the needs of rapid iteration and innovation due to their time-consuming, costly, and subjective preferences of designers. In view of this, we propose an innovative fabric design optimization simulation strategy aimed at breaking through these bottlenecks through technological means. This strategy cleverly integrates advanced technologies of variational autoencoder (VAE) and generative adversarial network (GAN). First, VAE is used to capture and learn the complex distribution characteristics of existing clothing fabric designs, which include key information such as fabric texture, color matching, and structural details. Subsequently, GAN uses the hidden vectors obtained from VAE as input to generate brand new fabric design samples. During the training phase, GAN continuously iterates and optimizes, engaging in intense “adversarial” interactions between its generator and discriminator. The generator is dedicated to creating new samples that are as close to real fabric designs as possible, while the discriminator is responsible for identifying the authenticity of these samples. This process is implemented through backpropagation (BP) loss function, ensuring that the generated fabric design can visually simulate real fabrics. Experimental verification shows that this method can not only effectively generate high-quality and realistic clothing fabric designs, but also greatly shorten the design cycle and reduce costs.

Assessing the impact of moisture buffering properties of materials on indoor environmental quality: A study on a recycled material plaster

This study examines Moisture Buffer Value (MBV) of interior finishing layers, which impacts buildings internal relative humidity, thus indoor environmental quality. The MBV depends on material's moisture capacity and vapour diffusion resistance factor, both of which depends on relative humidity. The paper aims to (i) characterize a new recycled material plaster that includes construction and food industry waste through laboratory measurements, (ii) use dynamical building simulations to quantify the impact of the MBV of existing and the new interior plaster on relative humidity in real design scenarios, and (iii) evaluate how changing the definition of the MBV to consider its dependence on indoor air relative humidity can improve its accuracy. Results show that the plaster's moisture buffering properties significantly reduce the variations of the relative humidity of interior climates compared to a vapour-tight finishing layer. The performances of the new plaster made with recycled materials are comparable to those of the other plasters. The new MBV definitions (“Dynamical MBV″ and “Summer/Winter MBV”) show a significantly improved correlation with the relative humidity variations of indoor climates of buildings, observed in dynamic simulations, with respect to the typically used practical MBV. The new definitions are therefore promising for practical applications.

Nonideal mixing effect based on Cholette’s model on Van de Vusse reaction selectivity dynamics in an isothermal continuous stirred-tank reactor

Abstract The nonideal mixing effect on the Van de Vusse reaction dynamic in a continuous stirred-tank reactor (CSTR) has rarely been studied in literature. This work presents the mixing operation condition and kinetic reaction constant effects on the Van de Vusse reaction selectivity dynamic in an isothermal CSTR based on Cholette’s model. The mixing condition effect is defined using the parameter μ = n/m, where n is the flow fraction to the reactor and m is the dead space fraction in the reactor. In Cholette’s model, μ = 1 and μ ≠ 1 represent, respectively, the ideal and nonideal mixing operations. Herein, the final desired product yield steady state in the phase plane is first constructed under reaction constant and mixing condition combinations, indicating that the larger the μ values, the higher the desired product steady-state yield. In the selectivity dynamic simulations, the phase plane is divided into transient extreme yield region and region of monotonic variation in yield. It is found for the first time that a higher μ value leads to a decrease in the transient maximum selectivity region area and an increase in the transient minimum selectivity region area in the phase plane. Moreover, the trajectory from the start up to the final steady state will be changed due to nonideal mixing. These results have not been published and have direct applications in real reactor design and operation. Since a CSTR with ideal mixing is hardly attainable in practice, our results benefit the real reactor design and startup policy development in the desired product transient maximum yield.

Implementing multiple imputations for addressing missing data in multireader multicase design studies

BackgroundIn computer-aided diagnosis (CAD) studies utilizing multireader multicase (MRMC) designs, missing data might occur when there are instances of misinterpretation or oversight by the reader or problems with measurement techniques. Improper handling of these missing data can lead to bias. However, little research has been conducted on addressing the missing data issue within the MRMC framework.MethodsWe introduced a novel approach that integrates multiple imputation with MRMC analysis (MI-MRMC). An elaborate simulation study was conducted to compare the efficacy of our proposed approach with that of the traditional complete case analysis strategy within the MRMC design. Furthermore, we applied these approaches to a real MRMC design CAD study on aneurysm detection via head and neck CT angiograms to further validate their practicality.ResultsCompared with traditional complete case analysis, the simulation study demonstrated the MI-MRMC approach provides an almost unbiased estimate of diagnostic capability, alongside satisfactory performance in terms of statistical power and the type I error rate within the MRMC framework, even in small sample scenarios. In the real CAD study, the proposed MI-MRMC method further demonstrated strong performance in terms of both point estimates and confidence intervals compared with traditional complete case analysis.ConclusionWithin MRMC design settings, the adoption of an MI-MRMC approach in the face of missing data can facilitate the attainment of unbiased and robust estimates of diagnostic capability.

Hope Has Augmented Reality Applications in Science Education Improved Academic Achievement? An Experimental Study

This research was conducted using the random pretest-posttest control group pattern of real experimental design, which is a subset of experimental research designs in the field of quantitative research. The study group consisted of students aged thirteen and fourteen in the eighth grades of private secondary schools affiliated with the Ministry of National Education in Turkey. Twenty students were selected for the experimental group, and twenty students were selected for the control group in an unbiased manner. However, in order to form homogeneous groups, the past academic records of the students of both experimental and control groups were examined before the students were randomly selected. Then, the random selection phase was started. An achievement test was developed for both the control and experimental groups to be used in the research. Additionally, augmented reality flashcards developed by FenAR related to solid, gas, and liquid pressures were used in the experimental group. The collected data were analyzed using the SPSS 25 package program. At the beginning of the study, it was determined that students' academic achievements were similar. Significant achievement was obtained in the experimental group, where augmented reality was used, compared to the class taught with a constructivist approach. Augmented reality, used as an educational tool, provided students with the opportunity to make abstract concepts more concrete and visually experience them. It can be concluded that especially complex science topics, when taught with augmented reality, become more understandable through 3D modeling and interactive simulations.

The Detection and Calculation of Design-Weighting on Indoor Environmental Elements—Illustrated by Real Interior Design Projects

In interior design projects, homeowners’ expectations and thoughts in mind are sometimes ambiguous and hard to pinpoint. This lack of clarity can lead to disagreements between owners and designers, making it difficult for designers to receive due remuneration. At the same time, the design project always has a restricted budget. Suppose the funds could be prioritized based on owners’ preferred weight of the indoor elements, with a larger portion of the budget allocated to the most crucial elements. In that case, it can significantly enhance the owner’s satisfaction. To address these challenges, this research introduces a novel analytical and computational approach that leverages the Positive And Negative Affects Schedule (PANAS) and Heart Rate Variability (HRV) to assess homeowners’ sentiments toward various indoor elements. This method aims to determine indoor elements’ design-weighting, design sequence, and distribution of project budgets, enabling designers to understand better and meet homeowners’ genuine needs and expectations in interior design projects. The efficacy of this approach was evaluated through the analysis of three real-life residential interior design projects. Data from the PANAS emotion scale and heart rate variability (HRV) measurements of the homeowners were collected before and after project completion. The findings revealed high satisfaction among homeowners following project execution. By adjusting indoor elements, owners’ negative emotions decreased significantly, positive emotions surged, and there was an improvement in the activity of sympathetic and parasympathetic nerves, leading to enhanced physical and mental well-being. Furthermore, in-depth interviews with homeowners corroborated the effectiveness of the analytical calculation method in identifying latent negative indoor elements of concern to homeowners, thereby validating the practical utility of this approach in real-world design scenarios.

ENRICHMENT OF SYSTEMS THINKING FOR PROBLEM SOLVING IN ENGINEERING SYSTEMS USING THE HERMENEUTIC LOOP: CASE STUDIES FOR DESIGNERS IN THE FIELD OF REMOTE COMMUNICATION

The systems thinking approach to problem solving is a dynamic and productive approach. However, emphasizing only the objective aspects of a phenomena and neglecting their subjective aspects can decrease its problem solving power. In order to enhance the problem solving power of systems thinking, it is proposed in this article to enrich systems thinking by using the hermeneutic loop. This applied research adopts practical aspects of systems thinking and hermeneutics in understanding and solving problems more effectively. The proposed approach is comprised of an additive loop, named the hermeneutic loop, concerning simultaneously “the whole system and its parts” and “subjectively and objectively”. Requirements of systems thinking are highlighted involving the holism and life cycle of the system. A summary of the considerations for systems thinking regarding holism and lifetime are as follows: (1) feedback in lifetime, (2) feed forward, (3) combining human and tools, (4) resources, (5) stock and flow and (6) carrying capacity (saturation). The utilization of this enriched systems thinking is investigated in three real engineering design processes, including case studies for designers in the field of remote communication. As the result, the proposed enriched systems thinking provides an improved understanding of the whole, which improves the solving power of the designers of components, and adds subjective product design to the objective design.

A hybrid quantum computing pipeline for real world drug discovery

Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing a hybrid quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.

CMGWO: Grey wolf optimizer for fusion cell-like P systems

The grey wolf optimizer is a widely used parametric optimization algorithm. It is affected by the structure and rank of grey wolves and is prone to falling into the local optimum. In this study, we propose a grey wolf optimizer for fusion cell-like P systems. Cell-like P systems can parallelize computation and communicate from cell membrane to cell membrane, which can help the grey wolf optimizer jump out of the local optimum. Design new convergence factors and use different convergence factors in other cell membranes to balance the overall exploration and utilization capabilities of the algorithm. At the same time, dynamic weights are introduced to accelerate the convergence speed of the algorithm. Experiments are performed on 24 test functions to verify their global optimization performance. Meanwhile, a support vector machine model optimized by the grey wolf optimizer for fusion cell-like P systems has been developed and tested on six benchmark datasets. Finally, the optimizing ability of grey wolf optimizer for fusion cell-like P systems on constrained optimization problems is verified on three real engineering design problems. Compared with other algorithms, grey wolf optimizer for fusion cell-like P systems obtains higher accuracy and faster convergence speed on the test function, and at the same time, it can find a better parameter set stably for the optimization of support vector machine parameters, in addition to being more competitive on constrained engineering design problems. The results show that grey wolf optimizer for fusion cell-like P systems improves the searching ability of the population, has a better ability to jump out of the local optimum, has a faster convergence speed, and has better stability.

VR-based Competence Training at Scale: Teaching Clinical Skills in the Context of Virtual Brain Death Examination

Teaching medical practical and soft skills in clinical routines is increasingly difficult, and manikin or actor-based simulations have gained popularity in the last decades. These simulations, however, hardly scale with the demand, are commonly insufficient to train crucial clinical competencies, and cannot portray complex visual and dynamic symptomatologies as required in, for example, brain death examinations. In this paper, we explore the requirements and challenges of integrating a large-scale high-throughput VR setup into a real medical curriculum and describe our approaches and implementation. Therefore we extend and evaluate an interactive virtual reality-based simulation for training brain death diagnostics in a virtual intensive care environment, featuring a fully reactive simulated patient. To enable the required scalability we integrated the simulation into a dedicated hardware and software framework, enabling 12 simultaneous VR trainings which are controlled by a centralized server system. Using this setup we continuously collected feedback on the application's usability and realism from hundreds of students to gain first insights into the applicability of large-scale VR-based learning systems in real course designs. After integrating this feedback, we conducted a controlled curricular study in which we compared the virtual brain death simulation with the classical manikin-based training approach. Our results indicate that the immersive learning experience is perceived to be more realistic and engaging and is overall preferred by the students while also providing the same learning effect as the alternatives.

Fine-tuning DSAE-based anomaly-locating model of underwater mooring system

Aiming at the difficulty of anomaly locating of the underwater mooring system, this paper develops an anomaly-locating algorithm based on the Deep Stark Auto-encoder (DSAE) network. Specifically, based on the real design parameters of a semi-submersible platform, a hydrodynamic model was established. Damage degrees of 5%was selected based on the DNV standard. The analysis of the responses was carried out when the anchor chain was damaged in different positions. Then an anomaly-locating model was established based on the DSAE method. The locating accuracy reached 99.13%. In contrast, the complete states were recognized as the damaged state. Furthermore, the established model was retrained by the fine-tuning operation. The locating accuracy reached 100% which can guide the safe service of the platform.

Impacts of dynamic stall on engineering model predictions of wind turbines loads under design load cases

The present work is aimed specifically at providing an order of estimate of the load changes due to the inclusion of unsteady aerodynamic effects. Two dynamic stall models are assessed: (1) the incompressible version of the Beddoes-Leishman model and (2) the newly developed IAG dynamic stall model. Two sets of attached flow constants are considered, based on the flat plate approximations according to Jones and the original constants adopted by the IAG model. On top of that, the influence of the impulsive contributions is also highlighted. This work is aimed at filling the gap of the state-of-the-art since at present there is no available study which compares the impacts of using the IAG model against the BL model in real design load case (DLC) scenarios required for wind turbine designs, considering it is very important to estimate loads changes depending on the engineering model being used. It will significantly influence the design of wind turbine structures, especially blades and the strength of the materials for the manufacturing process. Toward the end, providing a proper estimate into the load changes will greatly influence the cost required in blade design and manufacturing. The present study investigates the influence of dynamic stall modeling on the loads acting on wind turbines with various sizes, ranging from 2.5 MW to 8 MW of the rated power. In the studies, both onshore and offshore conditions are considered to obtain the full picture into the engineering modeling effects.

A complex network-based approach for resilient and flexible design resource allocation in industry 5.0

The development of Industry 5.0 focuses on customization, personalization in production, and the innovative thinking of employees, elevating the value of human contribution. Design, being an innovation-driven domain, demands greater flexibility in resource allocation. Consequently, rapidly and effectively allocating cloud service resources for personalized design tasks becomes crucial. With the emergence of the Industrial Metaverse, which blurs the boundaries between real and virtual design and manufacturing, it is gaining increasing attention. To embrace the advent of Industry 5.0 and the Industrial Metaverse, swift collaborative cloud services for design and manufacturing resources are essential. In this context, this article introduces a novel approach combining complex networks with the Non-dominated Sorting Genetic Algorithm III (NSGA-III), aimed at rapidly optimizing the dynamic allocation of distributed design resources (DRs). Initially, a multipartite graph is created from raw data and mapped to multiple bipartite graphs to identify key nodes in the network through intersection. Subsequently, these key nodes are used as reference points in the NSGA-III algorithm to achieve high-quality cloud service combinations, meeting the needs of design tasks with multiple subtasks, and related multi-objective optimization, including time, cost, reliability, maintainability, and reputation associated with the design. Finally, the Pareto service combinations obtained are used to construct a new complex network and employ the Girvan-Newman algorithm based on edge betweenness to identify community structures. In case of anomalies in the best service combination, alternative options can be swiftly searched from the identified communities, thereby enhancing the resilience of the cloud service process. Experimental results demonstrate the method's advantages in recovery and robustness, contributing significantly to the optimization of rapid cloud service allocation for DRs in the context of Industry 5.0.

Service Life Design of Reinforced Concrete Maritime Infrastructure: Holistic Approach with Experiences from Chilean Cases

The consideration of the useful life of reinforced concrete in the different stages of a structure's life cycle, as a distinguishing factor in the design process for performance under aggressive environmental conditions, has presented new challenges in construction. This includes the development of new regulations incorporating innovative engineering materials, performance tests and modeling processes to estimate concrete deterioration over time, and construction processes ensuring adequate Quality Control and Quality Assurance (QAQC). As a result, infrastructure projects have been able to exceed their intended lifespan, maintaining serviceability levels for more than 100 years. One such example is the Chacao Bridge project currently under construction in southern Chile. This paper provides an overview of project stages from a holistic approach, focusing on aspects inherent to planning including design and construction processes, control stages for monitoring deterioration levels through regular diagnostics and appropriate monitoring to support ongoing structural health management. Additionally, it presents real case design examples carried out with specific models created for each case.

AiTO: Simultaneous gate sizing and buffer insertion for timing optimization with GNNs and RL

Gate sizing and buffer insertion for timing optimization are performed extensively in electronic design automation (EDA) flows. Both of them aim to adjust the upstream and downstream capacitances of gates/buffers to minimize delay. However, most of existing work focuses on gate sizing or buffer insertion independently. This paper proposes a learning-based timing optimization framework, AiTO, that combines reinforcement learning with graph neural network, to perform simultaneously gate sizing and buffer insertion. We model buffer insertion as a special gate sizing by determining possible buffer locations in advance and treating the buffer insertion and gate sizing as an RL process. Experimental results on 10 real designs (28-nm and 110-nm) show that, AiTO can achieve better worst negative slack (WNS) optimization results than OpenROAD while being able to improve the results of the commercial tool, Innovus, to some extent. Moreover, ablation studies demonstrate the benefits of performing simultaneous gate sizing and buffer insertion for timing optimization.

Towards Controllable Generative Design: A Conceptual Design Generation Approach Leveraging the FBS Ontology and Large Language Models

Abstract Recent research in the field of design engineering is primarily focusing on using AI technologies such as Large Language Models (LLMs) to assist early-stage design. The engineer or designer can use LLMs to explore, validate and compare thousands of generated conceptual stimuli and make final choices. This was seen as a significant stride in advancing the status of the generative approach in computer-aided design. However, it is often difficult to instruct LLMs to obtain novel conceptual solutions and requirement-compliant in real design tasks, due to the lack of transparency and insufficient controllability of LLMs. This study presents an approach to leverage LLMs to infer Function-Behavior-Structure (FBS) ontology for high-quality design concepts. Prompting design based on the FBS model decomposes the design task into three sub-tasks including functional, behavioral, and structural reasoning. In each sub-task, prompting templates and specification signifiers are specified to guide the LLMs to generate concepts. User can determine the selected concepts by judging and evaluating the generated function-structure pairs. A comparative experiment has been conducted to evaluate the concept generation approach. According to the concept evaluation results, our approach achieves the highest scores in concept evaluation, and the generated concepts are more novel, useful, functional, and low-cost compared to the baseline.

A blockchain-based deployment framework for protecting building design intellectual property rights in collaborative digital environments

Protecting intellectual property rights (IPR) in the architecture, engineering, and construction (AEC) industry is a long-standing challenge. In the collaborative digital environments, where multiple professionals use digital platforms such as building information modelling to collaborate on a building design, this challenge has intensified. This research harnesses the functions of blockchain technology, such as consensus mechanisms, distributed broadcasting ledgers, cryptographic algorithms, and non-fungible tokens, to propose a blockchain-based framework to protect building design IPR in the AEC industry. Adopting a design science approach, a framework is proposed and then further developed into a system that is implemented, illustrated, and evaluated in a case study. The system uses non-fungible tokens to tokenize building design IPR and deploys blockchain’s decentralized consensus mechanisms, distributed ledgers, and cryptographic algorithms to safeguard the IPR and its transactions. This prototype system is found feasible with satisfactory performance in enhancing the efficiency of IPR registration and protection, reducing cost, improving information transparency, reinforcing immutability, and preventing non-valuable registrations. Researchers and practitioners are encouraged to develop the framework for different applications such as real-life design IPR protection and design management.

Exploring Student Technology and Engineering Literacy in Science Learning: an Overview of the Initial Study

Technological and engineering literacy is a capability that must be imparted to students in order to be able to compete globally and be able to solve 21st century problems. This study aims to describe aspects of technological and technical literacy of junior high school students. This type of research is descriptive quantitative analysis. Data were obtained from technological and technical literacy test instruments. The test instrument is adopted from tests issued by NAEP. Data were analyzed quantitatively. The research results show that 69.2% in the complete category, 15.4% in the Partial category and incomplete in the Understanding of Technology Principles aspect. The aspect of Developing Solutions and Achieving Goals as many as 54.8% of students fall into the complete category, 19.2% in the Satisfactory category, 15.4% the Essential category, 7.7% the Partial category, and 26.9% incomplete category. Meanwhile, the Communication and Collaboration aspect, 65.4% were in the complete category, and 34.6% were in the incomplete category. three types of assessment targets in the three main TEL assessment areas, with the most complete category being Gather and Organize data and information, followed by Identification of examples of systems or processes and Representation of alternative analysis and solutions. Science learning involving real experiences and design and engineering processes related to technological principles will make students technologically and engineering literate and can improve students' critical thinking and problem solving abilities.