Design Space Research Articles

Hybrid Surrogate Modeling Approach for Data Reduction and Design Space Exploration of Turbine Blades

Hybrid Surrogate Modeling Approach for Data Reduction and Design Space Exploration of Turbine Blades

A comprehensive validation of GRA with ant colony optimization for robust optimal design: A case study on the forming process

Sheet metal forming design involves numerous factors and controlling all these parameters to prevent cracks is a major challenge to maintain the quality. Therefore, the careful selection of process parameters is critical for ensuring product longevity. Several researchers employed different techniques for optimal selection of process parameters but consistency among the techniques and validation of results in real time scenarios will play a pivotal role in determining the suitable parameters. Therefore, the current work presents a validation method of Grey Relational Analysis (GRA) results with Ant Colony Optimization (ACO) for the robust optimum design with an illustrative example of forming process. Al 6061 – T6 alloy was chosen as a material for deep drawing process and L27 Orthogonal Array was planned to conduct experiments with four input parameters such as blank thickness, Die and Blank Temperature, Die Speed, Lubrication at three levels each. Punch force and Thickness variation were considered as the output parameters. GRA Technique was implemented to find the best optimal combination and regression equation was developed to predict the nature of objective function. These results were given as an input to ACO, a nature inspired algorithm to search for the optimal solution with in the design space. Sensitivity analysis has also been performed to highlight the effectiveness of the proposed solution in terms of solution quality and computational efficiency. The ACO method, GRA technique and the experimental results are almost similar with a margin of acceptable deviation. The results indicated the robustness of the projected approach in achieving the quality of solution.

Identification of Crashworthy Designs Combining Active Learning and the Solution Space Methodology

Abstract This study introduces a novel methodology for vehicle development under crashworthiness constraints. We propose coupling the solution space method (SSM) with active learning reliability (ALR) to map global requirements, i.e., safety requirements on the whole vehicle, to the design parameters associated with a component. To this purpose, we use a classifier to distinguish between the design that fulfills the requirements, the safe domain, and those that do not, the failure domain. This classifier is trained on finite element simulations, exploiting the learning strategies used by ALR to efficiently and precisely identify the border between the two domains and the information provided on these domains by the SSM. We then provide an exemplary application where the efficiency of the method is shown: the safe domain is identified with 270 samples and an average total error of 2.5%. The methodology we propose here is an efficient method to identify safe designs at a comparatively low computational budget. To the best of our knowledge, there is currently no methodology available that can identify regions in the design space that result in designs satisfying the local requirements set by the SSM due to the complexity and strong nonlinearity of crashworthiness simulations. The proposed coupling exploits the information of SSM and the capabilities of ALR to provide a fast mapping between the global requirements and the design parameters, which can, in turn, be made available to the designers to inexpensively evaluate the crashworthiness of new shapes and component features.

توظيف التكنولوجيا في تصميم الإعلان الرقمي وتمثلاتها الإبداعية في نتاجات طلبة التربية الفنية

The research dealt with the topic of (employing technology in the design of digital advertising and its creative representations in the products of art education students). The research contained four chapters. The first chapter dealt with: the methodological framework of the research, represented by the research problem that was revealed in the following question: How has technology contributed to employment in the design of digital advertising? They have no role in enriching the creative process and are represented in their artistic productions. The researcher determined the limits of the research, which were represented by the products of the students of the Department of Art Education - First Stage / College of Fine Arts/ University of Baghdad for the academic year (2020-2021), and then the researcher moved to the second semester, which included two sections. The title of the first section was employing technology artistically for advertising, and the section included the second is the creative process and aesthetic standards in the artistic achievement, and the researcher extracted a number of indicators that resulted from the theoretical framework: 1. The use of digital technical methods in employing shapes by following the technique of assembling and composing photographs, and generating a main rhythm between images and color values, served the functional aspect and the creative process of the advertising poster. 2. The computer showed a kind of diversity and excitement in the use of letters and their formal formulation in the design space. Among the most important conclusions is that the use of digital technology in the field of art education for practical academic subjects has given students the ability to diversify the styles of artistic production and facilitate the procedures used in producing these artistic works.

How to catch prospective use? A comparative study of virtual environment movie and guided imaginary for projecting future users in a public space

Focusing on the design of a public space, we propose a methodological contribution to integrate possible future users to studies in prospective ergonomics. Our objective is to establish a rigorous protocol for comparing two methods of projecting potential usage. First, a projection interview based on a virtual environment movie and second, a Guided Imaginary Projection interview. We sought to determine to what extent these two methods provide participants with the ability to live a ‘quasi-experience’ of the space. This ‘quasi-experience’ is measured by a questionnaire inspired by the concepts of presence and absorption and by the analysis of the embodiment level of the discourse produced during the interviews, from embodied to general discourse. The results show that while the two methods produce similar results in terms of the level of projection experience, the virtual environment movie projection produces significantly more general discourse.

Point-enhanced convolutional neural network: A novel deep learning method for transonic wall-bounded flows

Low order models can be used to accelerate engineering design processes. Ideally, these surrogates should meet the conflicting requirements of large design space coverage, high accuracy and fast evaluation. Within the context of aerospace applications at transonic conditions, this can be challenging due to the associated non-linearity of the flow regime. Different methods have been investigated in the past to predict the flow-field around shapes such as airfoils or cylinders. However, they usually have reduced spatial resolution, limiting the prediction capabilities within the boundary layer which is of interest for transonic wall-bounded flows. This work proposes a novel Point-Enhanced Convolutional Neural Network (PCNN) method that combines the advantages of the well-established PointNet and convolutional neural network approaches. The PCNN model has relatively low memory requirements in the training process, preserves the spatial correlation in the domain and has the same resolution as a traditional computational method. The architecture is used for the flow-field prediction of civil aero-engine nacelles in which it is demonstrated that the flow features of peak isentropic Mach number (Mis), pre-shock isentropic Mach number and shock location (X/Lnac) are captured within ΔMis = 0.02, ΔMis=0.04, ΔX/Lnac=0.007, respectively. The PCNN model successfully predicts the integral parameters of the boundary layer, in which the incompressible displacement thickness, momentum thickness and shape factor are typically within 5% of the CFD. Overall, the PCNN method is demonstrated for transonic wall-bounded flows for a range of flow physics that include shock waves and shock-induced separation.

Tscluster: A python package for the optimal temporal clustering framework

Temporal clustering extends the conventional task of data clustering by grouping time series data according to shared temporal trends across sociospatial units, with diverse applications in the social sciences, especially urban science. The two dominant methods are as follows: Time Series Clustering (TSC), with dynamic cluster centres but static labels for each entity, and Sequence Label Analysis (SLA), with static cluster centres but dynamic labels. To implement the universe of models spanning the design space between TSC and SLA, we present tscluster, an open-source Python framework. tscluster offers: (1) several innovative techniques, such as Bounded Dynamic Clustering (BDC), that are not available in existing libraries, allowing users to set an upper bound on the number of label changes and identify the most dynamically evolving time series; (2) a user-friendly interface for applying and comparing these methods; (3) globally optimal solutions for the clustering objective by employing a mixed-integer linear programming formulation, enhancing the reproducibility and robustness of the results in contrast to existing methods based on initialization-sensitive local optimization; and (4) a suite of visualization tools for interpretability and comparison of clustering results. We present our framework using a case study of neighbourhood change in Toronto, comparing two methods available in tscluster. Supplemental materials provide an additional case study of local business development in Chicago and a detailed mathematical exposition of our framework. tscluster can be installed via PyPI (pypi.org/project/tscluster), and the source code is accessible on Github (github.com/tscluster-project/tscluster). Documentation is available online at the tscluster website (tscluster.readthedocs.io).

Collaborative and integrated working between general practice and community pharmacies: A realist review of what works, for whom, and in which contexts.

Collaborative and integrated (C + I) working between general practice and community pharmacies has the potential to increase accessibility to services, improve service efficiency and quality of care, and reduce health care expenditures. Many existing studies report challenges and complexities inherent in establishing effective C + I ways of working. The aim of our review is to understand how, when and why working arrangements between General Practitioners (GP) and Community Pharmacists (CP) can provide the conditions necessary for effective communication, decision-making, and C + I working. We conducted a realist review to explore the key contextual factors and mechanisms through which GP-CP C + I working may be achieved. MEDLINE, Embase, CINAHL, PsycINFO, HMIC, Web of Science, IBSS, ASSIA, Sociological Abstracts, Sociology Database and the King's Fund Library Database were searched for articles and grey literature published between January 2000 and April 2022. A total of 136 documents were included in the final synthesis. Our findings highlight the importance of mutually beneficial remuneration models to support effective integration of services; supportive organisational cultures and values; flexible and agile IT systems/technologies; adequate physical infrastructure and space design to support multidisciplinary teamworking; the importance of establishing patient's trust in collaborative processes between GP-CP; and the need to acknowledge, support and utilise effective triadic relationships. Our research generates new insights regarding how, why and in which contexts C + I working can be achieved between GPs and CPs. The findings of our review can be used to inform future policy, research and clinical practice guidelines for designing and delivering C + I care.

Healthy urban blue space design: Exploring the associations of blue space quality with recreational running and cycling using crowdsourced data

Urban blue space offers substantial health benefits by encouraging population physical activity. Despite much evidence on the nature-health nexus, the relationship between blue space and recreational exercises remains under-studied, limiting the realisation of health benefits in blue space design. Using crowdsourced data, including volunteered geographic information and street view image data, this study investigates the associations of blue space quality with recreational running and cycling in Rotterdam, the Netherlands. Results show that recreational exercise levels on street segments vary based on the blue space type and design. Compared to inland canals and rivers, small-scale recreational waterbodies are more conducive to running but not cycling, while both activities tend to cluster around the Nieuwe Maas River. Interestingly, the Water View Index shows a general negative association with both activities after adjusting for the blue space type. Besides the waterbody characteristics, eye-level environmental factors, including higher Green View Index, lower building density, more diverse land use, greater connected street network and fewer traffic elements, are associated with more running and cycling exercises. Results for visual complexity and neighbourhood population composition are mixed depending on the exercise type. These findings are further translated into spatial design patterns for developing exercise-supportive and health-promoting blue spaces.

Comparative Analysis of Space Efficiency in Skyscrapers with Prismatic, Tapered, and Free Forms

This study offers a thorough comparative analysis of space efficiency in skyscrapers across three distinct forms: prismatic, tapered, and free. By examining case studies from each form category, this research investigates how architectural and structural design features impact space utilization in supertall towers. The findings reveal form-based differences in space efficiency and design element usage. In prismatic skyscrapers, which are primarily residential and utilize concrete outrigger frames, the average space efficiency was around 72%, with the core occupying 24% of the gross floor area (GFA). Tapered skyscrapers, commonly mixed-use with composite outrigger frames, showed an average space efficiency of over 70%, with a core-to-GFA ratio of 26%. Freeform towers, often mixed-use and using composite outrigger frames, demonstrated a space efficiency of 71%, with an average core-to-GFA ratio of 26%. Despite these variations, a consistent trend emerged: as the height of a building increases, there is a general decline in space efficiency, highlighting the challenges in optimizing space in taller structures. This analysis adds to the understanding of skyscraper design and space utilization, providing important insights for architects and urban planners aiming to improve the efficiency of future high-rise developments.

Computational design of 4D printed shape morphing lattices undergoing large deformation

Abstract In 4D Printing, active materials are embedded in structures such that the application of an external stimulus, usually coming from the environment, results in a structural response. To design structures that achieve a targeted shape change for a defined stimulus, also known as shape morphing, the material distribution and structure needs to be tuned. However, the computational design of such material distributions and structures is a challenging task and remains, despite recent advances, unable to fully leverage the entire design freedom offered by state-of-the-art 4D printing technology. Notable gaps concern the handling of large and complex deformations, the high computational cost, and the exploration of the design space by the generation of alternative solutions. In this article, a method is presented to fill this gap. First, an artificial neural net is trained that represents a deformation map that occurs during actuation. Then, a shape morphing truss is designed that achieves this deformation during actuation. The method is used to solve four shape morphing problems, where superior capabilities are demonstrated in terms of magnitude and complexity of deformations that can be handled, efficient generation of alternative solutions and versatility. Due to these capabilities, the method enables exploration of the full potential of 4D printing technology to create stimuli-responsive, multifunctional structures.

Dielectric Modulated Nanotube Tunnel Field-Effect Transistor with Core-Shell Cavity as a Label-Free Biosensor: Proposal and Analysis.

Dielectric Modulated Field-Effect Transistors (DMFETs) have emerged as promising candidates for label-free bioanalyte detection. However, the inherent short-channel effects in conventional DMFETs increase their static power dissipation significantly and limit their scalability and sensitivity. Therefore, FETs based on alternate conduction mechanism such as tunneling (TFETs), which are immune to the short-channel effects, appear to be a lucrative alternative to the MOSFETs for biosensing application. In this work, we propose a novel Dual Cavity Dielectric Modulated Nanotube Tunnel FET (DCDM NTTFET)-based label-free biosensor consisting of a Ge source and nanocavities within the core as well as a shell gate stack, which not only outperforms the conventional MOSFET and advanced nanowire (NW) TFET-based biosensors in terms of energy-efficiency and scalability but also exhibits a significantly high drain current sensitivity (SION = 2.9 × 108) and a threshold voltage sensitivity (SVth = 0.85), and a considerably high selectivity of more than 6 orders of magnitude. We also perform a comprehensive design space exploration for the proposed DCDM NTTFET and provide necessary design guidelines to further improve its performance considering the practical artifacts such as steric hindrance.

A 3D Spatial Model to integrate Stormwater Drainage Design into Public Space

Abstract. This paper explores the integration of water sensitive urban design within public space planning using integrated 3D digital technologies. In traditional design processes, drainage and public space design often utilise distinct software and are led by different professionals, which creates significant communication barriers.Although civil and hydraulic engineers have made significant strides in integrating hydrology analysis with multi-objective space design, the collaboration of conventional 2D hydrology with 3D landscape design still presents challenges. To address these challenges, this study proposes a novel approach that integrates drainage analysis and design within a 3D design model. This methodology is specifically designed to aid landscape and urban designers in incorporating stormwater management and drainage systems early in the design process. The approach involves three key steps: (1) identifying critical factors that impact, or are impacted by, drainage design; (2) translating these factors into specific spatial parameters; and (3) developing a workflow to integrate these parameters. This integration not only streamlines the design process, making it more cohesive and efficient but also mitigates potential conflicts between drainage requirements and spatial design, thereby reducing unnecessary revisions. Ultimately, this approach enhances the incorporation of essential drainage considerations into urban and landscape planning, leading to more sustainable and functional public spaces.

Real-Time Visibility Assessment in an Interactive Immersive Virtual Reality Application for Urban Public Space Design

Abstract. The design of urban public spaces (UPSs) aims to facilitate users’ physical and mental well-being by facilitating dynamic and stationary activities. The success of a UPS is often measured by its users’ engagement and experiences. However, UPSs frequently exhibit varying usage patterns, with some areas being heavily frequented and others neglected. This discrepancy is often due to users’ perceptions of safety, comfort, and liveliness. Failure to anticipate and address these aspects during the design phase of UPSs can result in dysfunctional urban spaces that fail to meet users’ needs. People experience UPSs primarily through their senses, with vision being the predominant sense and key to spatial cognition in UPSs. Therefore, adequate visibility of the spatial configuration must be integral to UPS design as a design goal. As a result of recent advances in Immersive Virtual Reality (IVR) end-users can participate in, interact and experience the possible UPS design scenarios, however, most IVR applications do not provide real-time feedback for design goals such as visibility. Established methods exist for analyzing visibility in UPS. However, these techniques are not usually integrated into VR apps to assess the effects of the designs. This paper addresses the gap for the lack of real-time visibility feedback within IVR applications for UPS design and its technical challenge. Building on a previous IVR application, CoHeSIVE, which was developed for UPS design, we integrated a ray-casting method and Unity’s built-in callbacks for dynamic visibility assessment of the features within the user’s field of view. The visibility assessment is then presented on a dashboard within the IVR app. This study serves as a cornerstone for future real-time feedback mechanisms of visibility assessment within IVR applications for urban design, facilitating informed design decisions.

Challenging Child-Friendly Urban Design: Towards Inclusive Multigenerational Spaces

The ongoing Covid-19 pandemic has not only presented novel challenges but has also brought to light previously unaddressed issues, such as children’s rights, their interdependence on adults, and the vulnerability of children concerning their mental well-being. The pandemic has served to accentuate the distinction between those spaces that have traditionally been designated for children or adults, and the manner in which they coexist. Some researchers posit that this phenomenon can be attributed to the emphasis placed on so-called child-friendly spaces. This article presents a critical examination of and challenge to the concept of child-friendly places, advocating for a shift towards multigenerational places. This critique draws on data from an observational study conducted in a community playground in Brno, Czechia. The concept of territorial production was employed as a tool to unveil the intricate assemblage of ever-changing control over territories and power dynamics within the playground among its visitors. The findings offer valuable insights into the practices through which children assert temporary control over spaces that are considered to be communal. Teenagers employ loud music or personal belongings to mark their territory, while younger children utilise movement to establish control. Territorial production coexists with those of the adults who also frequent the site. This highlights the necessity to create environments that are conducive to the needs of both children and adults, discouraging the design of exclusive spaces for children. The promotion of a multigenerational city can foster inclusivity, whereby the diverse needs and behaviours of different age groups within shared spaces are recognised and accommodated.

Research on the Design of Child-Friendly Landscape Space in Chengdu Crown Community Based on SDG

Objective: This study aims to explore and implement a child-centred landscape space design strategy to support the Sustainable Development Goals (SDGs), especially in the old residential environment of the Crown Community in Chengdu, China. Theoretical Framework: The study constructs a theoretical framework for child-friendly communities based on the United Nations' children's rights and the Sustainable Development Goals (SDGs), especially Goal 11 (Sustainable Cities and Communities) and Goal 13 (Climate Action). Method: Through literature review and field research, combined with questionnaires, this study collected 120 child samples, analyzed their behavioural needs, and proposed corresponding landscape space design strategies. Research Implications: The study found that the needs of children in the Crown Community mainly focus on natural experience, research experience, game interaction, and hiding exploration. The design strategy transforms the existing space into an art garden, a book garden, and a children's playground to meet these needs and promote the realization of SDGs. Research Significance: The study emphasizes that improving children's living environment through landscape design is significant to achieving the goals of sustainable cities and communities in SDGs. Originality/Value: This study provides an innovative approach to integrating SDGs into renovating old communities and offers a new perspective for designing child-friendly landscape spaces, which will help promote the renewal of old urban communities and the construction of child-friendly cities.

Enhancing Printability Through Design Feature Analysis for 3D Food Printing Process Optimization

We present a novel, systematic method for evaluating design printability in 3D food printing using a scoring system based on the Design for Additive Manufacturing (DfAM) guidelines. This study addresses a gap in the current literature by proposing a structured approach to assess and enhance the printability of 3D food designs. Our framework consists of a set of nine critical questions derived from the multi-level DfAM guidelines, focusing on key printability factors including unsupported features, geometric accuracy, and surface finish. The evaluation process converts qualitative assessments into numerical values, resulting in a comprehensive printability score that categorizes designs into high, moderate, or low printability levels. To validate the effectiveness of this method, we conducted a case study involving five different designs. The scoring system successfully explores the design space and maximizes the printability of 3D food products. This method alleviates the challenges in design evaluation compared with traditional trial-and-error approaches. The results demonstrate the practicality and efficiency of our framework’s output. The proposed methodology provides a structured approach to design evaluation, offering practical insights and a valuable tool for improving the success rate of 3D printed food products. This research contributes to the field by offering a systematic framework for assessing and enhancing the printability of 3D food designs, potentially accelerating the adoption and effectiveness of 3D food printing technology in various applications.

Engineering Interlayer Space and Composite of Square-Shaped V2O5 by PVP-Assisted Polyaniline Intercalation and Graphene for Aqueous Zinc-Ion Batteries.

V2O5 undergoes irreversible phase transition and collapse of layered structure during the Zn2+ insertion/extraction, which severely limits its application as a cathode for aqueous zinc-ion batteries (AZIBs). Herein, a synergistic strategy of conductive polyaniline insertion and graphene composite was proposed to boost the Zn2+ storage performance of the V2O5 cathode. A square-shaped polyaniline (PANI)-intercalated and graphene-composited vanadium oxide (GO/PANI-PVP/V2O5) structure was successfully synthesized via an in situ oxidation/insertion polymerization combined with a hydrothermal method. The results showed that PANI intercalation and the composite of graphene combined with layered V2O5, enabling reversible intercalation of Zn2+/H+. The insertion of conjugated PANI not only increases the lattice spacing of V2O5, providing a channel for rapid transport of Zn2+, but also increases the storage sites for charges through doping/dedoping processes and redox conversion reactions. GO/PANI-PVP/V2O5 delivers an excellent specific capacity (495 mA h g-1 at 0.1 A g-1), wonderful rate capability (208 mA h g-1 at 30 A g-1), and good cycling stability (93% after 4000 cycles). Our results provide a new approach for adjusting the valence states, interlayer spacing, and rational design of organic-inorganic compound materials for different functional materials.

Vertical Plant Configuration: Its Impact on Microclimate and Thermal Comfort in Urban Small Green Spaces

The presence of small green spaces throughout high-density urban areas is essential for enhancing thermal comfort and combating the urban heat island effect. Extensive research has been conducted on plants, which play a key role in cooling green spaces. However, the majority of research focuses on trees, overlooking the efficiency of vertical plant configurations, especially shrubs. Hence, this paper selected a typical small green space in Tokyo’s high-density urban area and employed scenario simulation to examine how various vertical plant configurations impact the microclimate and thermal comfort of small green spaces, with a particular emphasis on quantifying the efficiency of shrubs. The research primarily discovered four findings. First, trees significantly outperform shrubs in improving thermal comfort, especially during periods of strong sunlight during the day, leading to a direct modification of the thermal comfort levels. Second, while shrubs (height < 2 m) usually do not make a substantial difference in the overall thermal comfort levels of small green spaces, they are effective in reducing areas classified as “hot” and “very hot” during the day, with their cooling effect becoming more pronounced in the late afternoon. Third, when small green spaces combine trees and shrubs in their plant configuration, taller shrubs (within 0–2 m) exhibit a more pronounced cooling effect and improvement in thermal comfort. The conclusions of this study could offer guidance for the planning, design, and renovation of small green spaces in high-density urban areas.

Active learning accelerated exploration of single-atom local environments in multimetallic systems for oxygen electrocatalysis

Single-atom catalysts (SACs) with multiple active sites exhibit high activity for a wide range of sluggish reactions, but identifying optimal multimetallic SAC is challenging due to the vast design space. Here, we present a self-driving computational strategy that combines first-principles calculations and equivariant graph neural network (GNN) to explore over 30,000 binary metallic sites with varying combinations of 3d transition metals and different ligand environments for oxygen reduction and evolution reactions (ORR/OER). Active learning facilitates the investigation of the search space by balancing the exploration of unseen atomic structures with the exploitation of the active ones. The GNN learns the chemical environments to capture composition-structure-property relationships for ORR/OER activity and selectivity. The computational predictions of promising Co-Fe, Co-Co, and Co-Zn metal pairs are consistent with the state-of-the-art results of experimental measurements reported in the literature. This approach can be extended to a broader class of multi-element high entropic materials systems.