Design Tool Research Articles

Creative programming in architecture: a computational thinking approach

The creative programming language Processing can be used as a generative architectural design tool, which allows the designer to write design instructions (algorithms) and compute them, obtaining graphical outputs of great interest. This contribution addresses the inclusion of this language in the architecture curriculum, within the context of digital culture and alternative approaches to how digital tools are used and learned. It studies the different processes related to Computational Thinking that are triggered in the prototyping of computer applications and that lead to creativity. The similarity between architectural design and programming is analysed, both in problem solving (abstraction, decomposition, iterative revisions -debugging-, etc.) and in the use of mechanisms of a digital nature (loops, randomness, etc.). The results of the design and testing of a pilot course are shown, in which the way of teaching, learning and using this programming language is based on the graphical representation of problems through sketches.

A New Model for Building Energy Modeling and Management Using Predictive Analytics: Partitioned Hierarchical Multitask Regression (PHMR)

Buildings are major consumers of energy, accounting for a significant proportion of total energy use worldwide. This substantial energy consumption not only leads to increased operational costs but also contributes to environmental concerns such as greenhouse gas emissions. In the United States, building energy consumption accounts for about 40% of total energy use, highlighting the importance of efficient energy management. Therefore, accurate prediction of energy usage in buildings is crucial. However, accurate prediction of building energy consumption remains a challenge due to the intricate interaction of indoor and outdoor variables. This study introduces the Partitioned Hierarchical Multitask Regression (PHMR), an innovative model integrating recursive partition regression (RPR) with multitask learning (hierML). PHMR adeptly predicts building energy consumption by integrating both indoor factors, such as building design and operational variables, and outdoor environmental influences. Rigorous simulation studies illustrate PHMR’s efficacy. It outperforms traditional single-predictor regression models, achieving a 32.88% to 41.80% higher prediction accuracy, especially in scenarios with limited training data. This highlights PHMR’s robustness and adaptability. The practical application of PHMR in managing a modular house’s Heating, Ventilation, and Air Conditioning (HVAC) system in Spain resulted in a 37% improvement in prediction accuracy. This significant efficiency gain is evidenced by a high Pearson correlation coefficient (0.8) between PHMR’s predictions and actual energy consumption. PHMR not only offers precise predictions for energy consumption but also facilitates operational cost reductions, thereby enhancing sustainability in building energy management. Its application in a real-world setting demonstrates the model’s potential as a valuable tool for architects, engineers, and facility managers in designing and maintaining energy-efficient buildings. The model’s integration of comprehensive data analysis with domain-specific knowledge positions it as a crucial asset in advancing sustainable energy practices in the building sector.

An analytical model of oscillating heat pipe performance

Concentrated heat dissipation poses formidable challenges across various domains, such as electronics, power generation, and aerospace applications. Oscillating Heat Pipes (OHPs), also known as Pulsating Heat Pipes (PHPs), have emerged as promising solutions due to their exceptional heat transfer capabilities and adaptability. Although OHPs have received significant research attention, their widespread adoption in industry has been hindered by the absence of reliable and cost-effective design tools. These tools are challenging to develop due to the complex nature of OHP operation, the multitude of design variables to consider, and the substantial computational resources required for computational modeling.In this study, previous OHP models based on first principles and spring–mass–damper analogies are combined with novel methods to create an analytical model that captures a much wider range of OHP phenomena than previous analytical models. The model predicts OHP heat transfer performance orders of magnitude faster than computational techniques and for a wider range of design parameters than experimental correlations. Furthermore, this model provides critical insights into the underlying principles governing OHP operation, including a theory for the onset and stabilization of circulation.The analytical model was compared to 185 experimental data in the literature and predicted the OHP temperature drop with a mean error of 16.6%. Additionally, the temperature drop was predicted within 30% for 84.5% of the data analyzed. The model’s analytical nature facilitates rapid solutions, enabling evaluations across a large design space that could be used to identify a small number of candidate designs for further experimental or computational evaluation.

Dig-up Primers: A Pipeline for Identification of Polymorphic Microsatellites Loci within Assemblies of Related Species.

Simple sequence repeats (SSRs) have become one of the most popular molecular markers and are used in numerous fields, including conservation genetics, population genetic studies, and genetic mapping. Advances in next-generation sequencing technology and the growing amount of genomic data are driving the development of bioinformatics tools for SSR marker design. These tools work with different combinations of input data, which can be raw reads or assemblies, and with one or more input datasets. We present here a new strategy and implementation of a simple standalone pipeline that utilizes more than one assembly for the in silico design of PCR primers for microsatellite loci in more than one species. Primers are tested in silico to determine if they are polymorphic, eliminating the need to test time-consuming cross-species amplification in the laboratory. The end result is a set of markers that are in silico polymorphic in all analyzed species and have great potential for the identification of interspecies hybrids. The efficiency of the tool is demonstrated using two examples at different taxonomic levels and with different numbers of input assemblies to generate promising, high-quality SSR markers.

A novel mesoscale modelling method for steel fibre-reinforced concrete with the combined finite-discrete element method

This paper proposes a novel mesoscale modelling method to study the effect of steel fibres on the fracture properties of steel fibre reinforced concrete (SFRC) based on an integrated finite-discrete element method (FDEM) framework. The present method models the steel fibre of SFRC as multiple bar elements, while simulating the cement matrix using triangle elements. A comprehensive bond-slip model is also introduced between the triangle element and bar element, enabling accurate reproduction of the interaction behaviour between the concrete matrix and steel fibre. Additionally, the progressive failure process of cohesive elements in FDEM is considered as crack propagation, confirming the superiority of FDEM from continuum to discontinuum. The results of the Brazilian split test and uniaxial compression test validate the capability of the FDEM framework for mode Ι and mode II fractures, while single fibre pull-out tests and 3-point-bending beam tests provide validation of the effectiveness of the integrated FDEM framework for the mesoscale modelling of SFRC. The discussion also covers employing the mesoscale modelling method to investigate the effect of steel fibre on the fracture properties of SFRC. Owing to its merits in quantitative analysis, the present method can serve as a feasible potential tool for mesoscale modelling analysis and SFRC design.

Predictive Tool Use and Willingness for Surgery in Patients With Knee Osteoarthritis

Despite the increasing number of tools available to predict the outcomes of total knee arthroplasty (TKA), the effect of these predictive tools on patient decision-making remains uncertain. To assess the effect of an online predictive tool on patient-reported willingness to undergo TKA. This parallel, double-masked, 2-arm randomized clinical trial compared predictive tool use with treatment as usual (TAU). The study was conducted between June 30, 2022, and July 31, 2023. Participants were followed up for 6 months after enrollment. Participants were recruited from a major Australian private health insurance company and from the surgical waiting list for publicly funded TKA at a tertiary hospital. Eligible participants had unilateral knee osteoarthritis, were contemplating TKA, and had previously tried nonsurgical interventions, such as lifestyle modifications, physiotherapy, and pain medications. The intervention group was provided access to an online predictive tool at the beginning of the study. This tool offered information regarding the likelihood of improvement in quality of life if patients chose to undergo TKA. The predictions were based on the patient's age, sex, and baseline symptoms. Conversely, the control group received TAU without access to the predictive tool. The primary outcome measure was the reduction in participants' willingness to undergo surgery at 6 months after tool use as measured by binomial logistic regression. Secondary outcome measures included participant treatment preference and the quality of their decision-making process as measured by the Knee Decision Quality Instrument. Of 211 randomized participants (mean [SD] age, 65.8 [8.3] years; 118 female [55.9%]), 105 were allocated to the predictive tool group and 106 to the TAU group. After adjusting for baseline differences in willingness for surgery, the predictive tool did not significantly reduce the primary outcome of willingness for surgery at 6 months (adjusted odds ratio, 0.85; 95% CI, 0.42-1.71; P = .64). Despite the absence of treatment effect on willingness for TKA, predictive tools might still enhance health outcomes of patients with knee osteoarthritis. Additional research is needed to optimize the design and implementation of predictive tools, address limitations, and fully understand their effect on the decision-making process in TKA. ANZCTR.org.au Identifier: ACTRN12622000072718.

Targeting of G-quadruplex DNA with 99mTc(I)/Re(I) Tricarbonyl Complexes Carrying Pyridostatin Derivatives.

The main goal of this work was to elucidate the potential relevance of (radio)metal chelates of 99mTc and Re targeting G-quadruplex structures for the design of new tools for cancer theranostics. 99mTc provides the complexes with the ability to perform single-photon-emission computed tomography imaging studies, while the Re complexes should act as anticancer agents upon interaction with specific G4 DNA or RNA structures present in tumor tissues. Towards this goal, we have developed isostructural 99mTc(I) and Re(I) tricarbonyl complexes anchored by a pyrazolyl-diamine (Pz) chelator carrying a pendant pyridostatin (PDS) fragment as the G4-binding motif. The interaction of the PDF-Pz-Re (8) complex with different G4-forming oligonucleotides was studied by circular dichroism, fluorescence spectroscopy and FRET-melting assays. The results showed that the Re complex retained the ability to bind and stabilize G4-structures from different DNA or RNA sequences, namely those present on the SRC proto-oncogene and telomeric RNA (TERRA sequence). PDF-Pz-Re (8) showed low to moderate cytotoxicity in PC3 and MCF-7 cancer cell lines, as typically observed for G4-binders. Biodistribution studies of the congener PDF-Pz-99mTc (12) in normal mice showed that the complex undergoes a fast blood clearance with a predominant hepatobiliary excretion, pointing also for a high in vitro stability.

Developing an integrated digital delivery framework for designing and planning construction projects: a review of interoperable digital technologies

The emergence of industry 4.0 has given rise to the application of various digital technologies in the construction industry (CI). However, the integration of these different digital technologies towards the delivery of project objectives has been hampered over the years, especially at the design and planning stages of construction. Thus, the concept of integrated digital delivery (IDD) has been proposed as a means of integrating digital technologies to enhance construction process workflows and productivity through collaboration. This study conducted a critical review of extant literature of IDD application in the CI to identify the technologies which can be readily integrated at the design and planning phase of IDD. This study aims at proposing a system architecture design for the application of digital technologies at the design and planning phase of IDD. To achieve this aim, the study employed a systematic review to analyze 63 papers obtained from Scopus and Web of Science databases. Subsequently, content analysis was used to ascertain prospects and opportunities for integrating and interoperating the identified technologies. This study identified BIM, VR/AR, Digital Twin, Big Data and CPS as the most prominent interoperable technologies for digital design and planning in IDD. This study contributes to the body of knowledge by providing a framework for integrating and adopting these technologies at the digital design and planning phase to improve project performance. This serves as a roadmap for the adoption of digital tools in digital design and planning.

Sustainability and Resiliency Investigation of Grouted Coupler Embedded in RC ABC Bridge Pier at Vehicle Impact

Increased occurrences of natural and man-made dynamic loading caused by high strain rate dynamic impact load have been recently reported. Different statistical results indicate that vehicular impacts occur more frequently compared to the other dynamic loads. However, existing literature primarily focuses on enhancing survivability and determining damage levels specifically in relation to faster bridge construction methods used as an accelerated bridge construction (ABC). In this context, the present study investigates the dynamic behavior of a commonly used connection type in ABC, namely grouted coupler that connects pre-cast elements like bridge piers and foundations. To execute the research, both the static and dynamic performance of grouted couplers embedded in pier foundation subjected to high strain rate loading incurred by high velocity vehicular impact are examined. A representative non-traditional reinforced concrete (RC) bridge pier is selected for the study with the standardized geometry and selected material properties. The use of splice sleeves as coupler materials and specified cross-sectional hollow cast iron cylinders filled with high strength concrete grout is employed for developing Finite Element (FE) modeling, extracting data from published journals. A commercial software, ANSYS, has been utilized to develop FE models to capture post impact respective static and dynamic behaviors, and the simulation results are then compared with the analytical. This also includes determining material performance via FE simulations. By considering dynamic loading, the dynamic impact factor (DIF) has been evaluated for the reinforcing steel bar adjacent and embedded into the coupler. In addition, dynamic simulations, and material modulus in demand to sustain impact are determined. Thus, the research necessitates mesh-independent sensitivity studies to investigate DIF corresponding to the precise outcomes. The findings of this study manifests valuable information that aids to opt for the suitable coupler connections, considering material properties, and adequate post impact execution. Consequently, it will serve as a useful design tool for design offices, structural practitioners, and forensic structural engineers.

Integrated CFD-FEM approach for external gear pump vibroacoustic field prediction

In this work, a three-dimensional fluid-structural and vibro-acoustics coupled model of a gear pump is presented. Gear pumps represent the majority of the positive displacement machines used for flow generation in fluid power systems. Typically, the dynamics of gear pumps are dependent on the characteristics of the fluid dynamics inside the pump, which translates into vibrations to the housing. These vibrations then propagate to the surrounding medium and emit sound. The purpose of this study is to propose a three-dimensional fully integrated computational model to simulate the complete gear pump behaviour from the fluid dynamics to the structural vibrations, up to the vibroacoustic response. In this hybrid configuration, a transient CFD (Computational Fluid Dynamics) model is first developed to simulate the 3D flow field with a deforming and re-meshing approach to take into account the variation of the volume between the gears. Second, the internal pressure field calculated at each time step is then used as a loading into a structural FEM (Finite Element Method) model of the gear pump to compute the actual stresses and deformations of the housing. Third, the results of the structural response are used as excitation in a vibroacoustic sub-model to simulate the radiated noise using a high-order FEM technique. The comparison between the numerical and experimental flow curves validates the CFD model. The sound power calculations from the vibroacoustic model show good comparison with the sound intensity measurements around the pump casing, confirming the validity of the proposed coupled model. The described CFD-FEM approach proves to be a powerful gear pump design tool: it provides a reliable estimate of gear pump working parameters such as fluid power and vibroacoustic characteristics, starting from the CAD (Computer-Aided Design) geometry of the components. Furthermore, being one of the first multi-domain simulation models of a gear pump, this work can be useful to researchers as a starting point to correlate the noise emitted with the internal flow in full 3D conditions.

Quality by Design Tool Assessed Ultraperformance Liquid Chromatography Method for the Analysis of Remogliflozin and Teneligliptin in Oral Dosage Form.

The UPLC methodology was used to establish a method for determining the qualitative and quantitative content of teneligliptin and remogliflozin tablets in oral solid dose form, as no simultaneous method was available. The developed liquid chromatography method consists of an X-Bridge C18 100 mm × 3.5 mm, 2.1 mm column with an economical 0.2 mL/min flow rate. A wavelength of 248 nm was used for detection, and the temperature of the column compartment was 30 °C. The method was evaluated using a static tool quality by design after it was validated as per the regulations. The data from validation result in linearity for both analytes with a correlation coefficient of more than 0.999. The accuracy data were found from a minimum of 98.1 to a maximum of 100.9. All of the validation results met the acceptance criteria. The stability of the analytical solutions proved for 24 h at bench and refrigerator temperatures. Studies of force degradation proved the stability indicating the nature of the method. A factorial design was used to evaluate the method performance.

Semi-automatic Hydraulic Hotpress Tool Design for the Production of Biodegradable Tableware from Kitchen Waste

The disposal of nonbiodegradable wastes in densely populated regions of the world has become a significant challenge for waste management systems due to the use of plastic composite materials in packaging. The demand for eco-friendly bio-composite packaging materials with biodegradable properties has increased to replace the petroleum-based synthetic polymers. Meanwhile, different varieties of food waste can be utilized to obtain biodegradable tableware. This study involves the production of biodegradable tableware through the application of pressure and temperature on the fruit peel or other alternative resources as substitutes. As a result, the utilization of a hot-pressing apparatus is imperative. An assessment was made on the effectiveness of a semi-automatic hydraulic hot-pressing machine in creating biodegradable cutlery from alternative resources. This hot-pressing machine was created through a literature review, working drawings, manufacturing design, and functional testing. Through the utilization of a hydraulic mechanism, the machine can apply a pressing force of up to 5 tons and generating temperatures of up to 120∘C. The contact time can be adjusted up to 60 mins with a heater cross-sectional area of 200 cm2. In addition, this hot-pressing machine can produce tableware in accordance with exact specifications.
 Keywords: biodegradable tableware, hot-pressing machine, organic waste

Design and Manufacture of Virus Sterilization Machine on Packaging to Prevent Transmission of Viruses and Bacteria in the Community

The increasing types of viruses and bacteria such as flu, tuberculosis or even the COVID-19 virus are quite worrying and the potential for this virus to spread is very large through product packaging, so manufacturing of virus and bacteria sterilization machines for product packaging can be a solution. Exposure to ultraviolet (UV) light type C with wavelengths below 260 nm can kill bacteria, viruses, and fungi. This research aims to create a virus and bacteria sterilization machine for packaging. The technology used is using automatic control, electric motor, conveyor, ergonomic frame, and ultraviolet (UV) light type C. This research was done by making design concepts related to machine components, making sterilization tool designs using Autocad software, and making tools to test virus sterilization machines. The creation of this machine can be an effective way to prevent the spread of viruses and bacteria that are carried and attached to the packaging during distribution.
 Keywords: Keywords: machine; sterilization; virus; bacteria; packaging; ultraviolet; UV

Improved approach to magnetorheological finishing of blind-hole cavities

ABSTRACT Nanofinishing of blind-hole cavities is important to produce high quality products without any surface irregularities. The commonly used methods such as grinding or honing are not efficient because of the uncontrolled forces and direct contact of the solid abrasive tool with the workpiece. Magnetorheological (MR) polishing process using smart fluid has proved its effectiveness in achieving the nano-finished surface. The existing tool design uses two different operations for nano-scale finishing of a blind-hole cavity, resulting into a higher production cost. In this present work, a novel tool that can vary its dimensions during finishing operation was developed for enhancing the surface finishing of blind-hole cavities. Distribution of magnetic fields within the entire tool and blind-hole cavity surface was found appropriate and also revealed that the tool surface has maximum magnetic field for stiffening the MR polishing fluid over its surface. After 90 minutes of finishing, it was concluded that the roughness (Ra) change on the blind-hole cavity were reduced by 75.12% (on inner vertical) and 74.21% (on inner bottom).

A Study on Factors Influencing Designers’ Behavioral Intention in Using AI-Generated Content for Assisted Design: Perceived Anxiety, Perceived Risk, and UTAUT

This study aims to comprehensively understand the intention to use Artificial Intelligence Generated Assistance in Design Tools (AIGC) among design students and practitioners, along with its influencing factors. Utilizing Smart-PLS software and Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, we constructed a comprehensive research model. Based on 404 valid questionnaire responses, we systematically analyzed the underlying mechanisms of designers’ attitudes towards AIGC tools. The sample encompasses diverse schools and levels of professional experience, ensuring the wide applicability of research outcomes. In the data analysis process, professional statistical analysis methods, including path analysis and standardized path coefficients, were employed to ensure a profound exploration of research questions. The results indicate that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly positively influence the willingness to use AIGC tools, while perceived anxiety and perceived risk exert negative impacts. This study, by integrating traditional and novel factors, provides crucial theoretical and practical guidance for the actual application of AIGC technology in the design field, offering profound insights for the future development and education of design technology.

Circularity Micro-Indicators for Plastic Packaging and Their Relation to Circular Economy Principles and Design Tools

Circularity Micro-Indicators for Plastic Packaging and Their Relation to Circular Economy Principles and Design Tools

BAUSTEIN—A design tool for configuring and representing design research

AbstractToday's Information Systems (IS) design research projects pursue digital innovation to conquer complex societal challenges. Many of these projects reach out beyond disciplinary and organisational boundaries, as evident in interdisciplinary consortia and academia‐industry collaboration. The design activities in each project differ based on contextual requirements and the team's underlying design logic. As diversity increases, shared understanding is essential for project success. Established design research methodologies need complementary tools to support design researchers in configuring their design activities and representing them faithfully, dimensions that contribute to a shared understanding. This article presents Baustein as an instance of such design tools. Baustein is tailorable to the contextual requirements of each design research project, comprising an ensemble of card‐deck, ready‐made configurations, and a manual. To ensure theoretical and practical relevance, the design of Baustein is based on primary empirical data (workshop and interviews with 16 IS design researchers) and a literature analysis of 99 published IS design research projects. We demonstrate its proof‐of‐value through three main evaluation episodes, altogether involving over 110 IS design researchers. With Baustein, design research teams can balance the trade‐off between creative messiness and standardised configurations of design activities.

An analytical method for free vibrations of the fuel rod with non-uniform mass of small modular reactor

The intricate internal structure of fuel rods results in a non-uniform mass distribution, making it imperative to employ analytical methods for accurate assessment. The study utilizes Euler beam theory to derive the transverse vibration equation for beams with varying mass distribution. The approach involves transforming the non-uniform mass beam into a multi-segment beam with concentrated mass points. Modal function relationships between adjacent uniform segments are established based on continuous conditions at connection points. This transformation leads to the conversion of the variable coefficient differential equation into a nonlinear matrix equation. The Newton-Raphson method is then applied to calculate the characteristic equation and mode shapes, essential for determining natural frequencies. To validate precision, the results obtained are compared with those derived from the finite element method. Furthermore, the developed method is employed to assess the impact of gas plenum location and length on the natural frequency of fuel rods. The proposed methodology serves as a rapid design tool, particularly beneficial during the design phase of fuel rods with non-uniform mass distribution, aiding in configuring structural aspects effectively.

DeltaLCA

Reducing the environmental footprint of electronics and computing devices requires new tools that empower designers to make informed decisions about sustainability during the design process itself. This is not possible with current tools for life cycle assessment (LCA) which require substantial domain expertise and time to evaluate the numerous chips and other components that make up a device. We observe first that informed decision-making does not require absolute metrics and can instead be done by comparing designs. Second, we can use domain-specific heuristics to perform these comparisons. We combine these insights to develop DeltaLCA, an open-source interactive design tool that addresses the dual challenges of automating life cycle inventory generation and data availability by performing comparative analyses of electronics designs. Users can upload standard design files from Electronic Design Automation (EDA) software and the tool will guide them through determining which one has greater carbon footprints. DeltaLCA leverages electronics-specific LCA datasets and heuristics and tries to automatically rank the two designs, prompting users to provide additional information only when necessary. We show through case studies DeltaLCA achieves the same result as evaluating full LCAs, and that it accelerates LCA comparisons from eight expert-hours to a single click for devices with ~30 components, and 15 minutes for more complex devices with ~100 components.

Influence of cutting tool design on ultrasonic-assisted drilling of fiber metal laminates

Ultrasonic-assisted drilling (UAD) is a machining process that is known to improve the hole quality and reduce cutting forces. Previous studies focused on optimizing cutting parameters to improve the hole quality in conventional drilling (CD) and UAD, as well as to finding the optimum vibration parameters (frequency and amplitude) that will increase the effectiveness of the UAD process. However, the influence of cutting tool type during UAD has been largely overlooked. This research aims to address this gap by analyzing the effect of cutting tool type during UAD on the cutting forces and hole quality in GLARE (Glass Laminate Aluminum-Reinforced Epoxy) laminates. Four types of drills, namely, twist drill (TD), double cone drill (DCD), a step drill type 1 (SD1), and step drill type 2 (SD2) with different step length, were selected for this study. The lowest thrust force (47.04 N) and torque (0.079 Nm) were achieved using twist drill, while DCD, SD1, and SD2 exhibited higher thrust forces (12.81%, 20.69%, 41.3%) and torques (94%, 92%, 91%), respectively. In addition, TD produced high-quality holes with lowest surface roughness (Ra 1.66 μm, Rz 10.58 μm) and minimal burr formation (entry burr height 152.3 μm, exit burr height 69.22 μm). Conversely, DCD, SD1, and SD2 showed higher surface roughness Ra (23%, 16%, 24%) and Rz (16%, 37%, 29%), respectively, compared to the TD. Holes drilled using SD1 and SD2 generally had smaller burr height. Overall, UAD system effectively reduced cutting forces at low spindle speed and feed rate. To achieve higher drilling quality, specifically to reduce the surface roughness and exit burr height, a medium spindle speed of 3000 rpm, a feed rate of 225 mm/min is recommended. Drilling at higher cutting parameters using UAD resulted in a decline in hole quality, except for entry burr height.