Design Tool Research Articles

Novel rod-sprung-mass model to investigate characteristics of building structural vibration induced by railways

Railway-induced vibrations in building structures require predictions to assess the serviceability. Fundamental properties such as the predominant modes of railway-induced vibrations are critical in structural design and mitigation approaches. However, the existing numerical models are customised to specific buildings. This renders these models unsuitable for broader mechanistic investigations. To address this, this study proposes a rod-sprung-mass (RSM) model to investigate the dynamic behaviour of structures under vertical ground excitations induced by railways. The columns were modelled as axial vibration rods, with each floor suspended on the rod as a sprung mass. The model was validated using a three-story steel frame structure and a five-story concrete structure. Subsequently, the dynamic characteristics of the structures and railway-induced responses were discussed. The structural vertical modes are attributed to the interaction between the columns and slabs, and the structural natural frequencies are outside the frequency range of the individual components. Parametric studies revealed that lower-order structural modes are generally predominant. Under such circumstances, the floor response tends to first decrease and subsequently increase with the floor height. As a result, low-rise buildings exhibit the maximum amplitude on the top floor, whereas the maximum amplitude is on the ground floor for high-rise buildings. Meanwhile, when structural high-order modes become predominant owing to a high-frequency input, the responses of the middle floors intensify, and the peak responses can be altered to the middle floors. This study emphasises the necessity of considering vertical global modes, whereas existing studies have focused mainly on mode shapes on particular floors. The RSM model is an efficient tool for structural design and vibration control.

Military Staff Study as a Tool for Language Policy Design: A Case of Establishing the RTN’s English Tests

A staff study (SS) is an administrative tool used for decision making which involves data gathering and analysis to determine the proper resolution to an issue. SS is also used in the areas of the military which are mostly assigned with classified information; as a result, resources regarding staff studies are still restricted or available in limited amounts. Furthermore, SS is rational and applicable in interdisciplinary academic fields; especially, in an area of policy design. This article proposes the value of such a study in education policy making through its application in the specific investigation of English testing development in the Royal Thai Navy (RTN). It articulated that a SS shares similar purpose to applied research in language education; yet, it is more focused on applying knowledge to problem solutions and circumstances. Though SS does not prepare to build or extend knowledge and expertise, its principle enacts a logical model guiding all existing context variables into the study. Thus, it can provide a thinking springboard for solving problems in language education. Finally, it can create a possible innovative way to deal with unique problems while further experimental process with the data analysis can justify research in language education.

Role Transformation of Teachers in Higher Vocational Colleges under Artificial Intelligence Assisted Art Creation: A Qualitative Study of Art Design Teachers

Artificial Intelligence (AI) technology has made remarkable progress in image processing, speech synthesis, intelligent creation, etc., and has shown great potential for application in artistic creation. In the teaching scenario of higher vocational colleges, teachers not only bear the responsibility of cultivating students' hands-on ability and creative thinking but also need to constantly adapt to the teaching changes brought about by technological advances. With their strong digital intuition, creative thinking and flexible adaptability, young art design teachers can make better use of AI technology to assist artistic creation, improve teaching quality, and enrich students' creative learning experience. Traditional art education usually relies on teachers to impart knowledge, guide practice, and evaluate works in the classroom; however, the widespread application of AI technologies has brought about a revolutionary transformation in art education, and new educational resources such as generative design tools and online assisted design platforms are gradually changing the traditional teaching mode and the role of teachers. Using self-efficacy theory and social cognitive career and motivation theory as a framework, this study investigated twelve in-service young art design teachers through 1) semi-structured interviews; 2) focus group discussions, and 3) member-checking interviews. The researcher analysed the data using a general inductive approach to capture the following core themes: 1) new role orientation; 2) continuing professional development and 3) support from society and educational institutions. Institutional support. The results of the study help to deeply understand the relationship between AI and art education in higher vocational colleges, clarify the self-worth positioning of young art design teachers in the new era, and provide theoretical support and practical guidance for the transformation of teachers' roles and the professional development of art education.

A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications.

Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces (BCI). Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.

Techno-economic design and placement tool for energy recovering pressure regulating turbines in water distribution systems

Water distribution systems present poor energy efficiency due to the significant amount of energy that dissipates in the form of excess water pressure. Besides energy losses, excess pressure increases water losses due to leakages and may result in pipeline damage. The employment of micro-turbines that concurrently harness excess energy and achieve pressure management is proposed by many researchers as potential replacements to the currently used pressure reduction valves. This work relies on previous work that determines the optimal design and operating point of such turbines integrated in the power distribution system, in order to develop an algorithm that considers the economic viability of such projects. The suggested algorithm has been applied to a simulated large-scale WDS in Kentucky that contains several locations where pressure regulation is currently performed or planned due to immense local pressure. Involving the economic viability of pressure regulating turbines within the design optimization process improves pay-back period and levelized cost of energy by more than 10 % and 20 %, respectively, compared to when their design is optimized solely for pressure regulation and maximum energy yield. The application of this approach to a typical WDS allowed more than 161 MWh/year of clean energy to be produced.

Conceptualising Intercultural Literacy: Narratives from English Language Teachers

This current study investigates how English language teachers in Indonesian higher educational institutions define and conceptualize intercultural literacy and practice it in their classrooms. Eight ELT practitioners were interviewed through narrative inquiry, and the data was analyzed using thematic analysis to explore their lived conceptualizations. The findings suggest three major themes on intercultural literacy, including conceptualization of intercultural literacy, implementation in professional practice, challenges in fostering intercultural understanding, and strategies to overcome these challenges. Teachers defined intercultural literacy as encompassing cultural understanding, adaptability, comparative analysis, and professional knowledge. They integrated it into their teaching through curriculum design, student-centered methods, and digital tools. This was done by weaving these into the curriculum design and retraining pedagogy with student-centric methods and other digital tools. However, the participants mentioned that they faced some obstacles, including institutional barriers, limited funding, and the difficulties of switching from the traditional to the newest teaching method. These issues were mitigated by professional development, collaboration, and inclusive instructional materials. The current research finding indicates the importance of comprehensive professional development and institutional support for teachers in developing intercultural competence in their students. These findings contribute to intercultural literacy in Indonesia and practical recommendations for improving it within Indonesian higher education. The finding highlights the how to prepare students for a globalized world through intercultural literacy and educator support.

Innovating Pharmaceuticals: The Rise of 3D printing in Drug Delivery.

Additionally known as three-dimensional (3D) printing, additive manufacturing has made major advancements possible in the fields of engineering, business, the arts, education, and medical. Thanks to recent developments, it is now possible to print three-dimensional to create complex, useful living tissues, biocompatible substances, cells, and supporting structures are combined. Regenerative medicine is utilizing 3D bioprinting. Additive manufacturing technology, or 3D printing, has been labelled the “next big thing” and is predicted to overtake cell phones in popularity. 3D printers use digital templates to produce actual, three-dimensional items. Adding layers to a print, commonly referred to as additive manufacturing, allows for the use of more than a hundred different materials, including nylon, metal, and plastic. Applications for 3D printing can be found in many different industries, such as industrial design, manufacturing, dental, automotive, aerospace, civil engineering, education, jewellery, footwear, and geographic information systems. It has shown to be a simple and affordable solution for a variety of use cases. Using computer-aided design tools and programming, three-dimensional printing is a sophisticated technique that adds material to a base surface to create three-dimensional things. Additive layer manufacturing, also referred to as 3D printing, is the technique of creating three-dimensional things by depositing or solidifying material one layer at a time. Using a computer-aided design module, pharmaceutical components are organized in a three-dimensional pattern. Afterwards, the constituents are converted into a machine-readable format resembling the surface of a three-dimensional dosage form. 3D printing has been used for jewelry, shoe-making, architecture, engineering & construction, the automotive industry as well as the aerospace field, dentistry and medicine, plus geographic information systems (GIS), civil engineering and education. After that stage of the process is completed, the surface transferred to the machine is then printed in different layers. Bioprinting is an interdisciplinary domain that integrates additive manufacturing with biology and material sciences to manufacture threedimensional structures representative of living organisms. The ability to create biological tissues and organs has attracted considerable attention in biomedical research owing to the rising demand for personalized medicine. This scenario propelled bioprinting forward which received much interest thus triggering comprehensive research efforts by various players such as companies, universities as well as research institutes. The goal of this book is to provide a thorough analysis of the complex and rapidly evolving field of bioprinting by critically analyzing and evaluating the existing scientific literature.

Guest editorial: Contemporary issues and emerging design tools in aviation sectors

Guest editorial: Contemporary issues and emerging design tools in aviation sectors

DoE-based modeling of cracked 3D orthotropic panels with stiffeners

This study uses the Design of Experiments (DoE) technique to develop analytical formulations for the stress intensity factor and other non-singular variables near the crack front of a stiffener-reinforced three-dimensional orthotropic panel. The method employs a 52 fractional factorial design, effectively reducing the number of finite element simulations required. The input variables consist of four parameters, each with five levels, which directly affect the process responses. The statistical analysis is performed using the Ellistat software, which offers powerful tools for Design of Experiments (DoE) and incorporates specialized algebraic-statistical approaches. These techniques are utilized for analysing the outcomes and formulating a mathematical model of the cracking characteristics for panels that are strengthened with five stiffeners. The findings of the analysis of variance (ANOVA) indicate strong statistical significance for all tested models, hence verifying their validity based on statistical evaluation criteria. In addition, the residual analysis demonstrates a normal distribution, which strengthens the reliability of the underlying models and fulfils a vital requirement for numerous statistical analyses. The analytical formulations that come from this study have enormous potential for practical use in the field of engineering, specifically for structures that are orthotropic and feature cracks. This study signifies a notable progress in integrating numerical simulation, statistical analysis, and mathematical modelling to develop pragmatic and dependable analytical instruments for the examination of intricate systems.

Application of multimodal learning in daylight provision and view quality assessment of residential building layouts

Optimizing the layout of residential buildings based on daylight performance and view quality is crucial to visual comfort and well-being of building occupants. Machine Learning (ML) methods offer valuable support for performance-based decision-making process at the early-stage building design. In this study, a novel workflow is introduced to integrate ML models into the architectural design process. With the designer’s input floor layout designs, the presented multimodal ML model predicts daylight provision and view quality, which are then translated into practical visual representations by a post-processing step. This approach allows input designs to be evaluated by the ML model, leading to enhanced design decisions while preserving the designer’s autonomy. Results for the best-performing model, implementing ResNet50 and a fully connected network, led to a Mean Square Error (MSE) of 0.0440 and 0.0478, and an R2 score of 0.7411 and 0.7815 for the daylight and view metrics, respectively. The results of the daylight and view predictive models are further interpreted according to different apartment categories and at various resolutions. These results indicate that the method could be viable for predicting daylight provision and view quality in early design tools, providing designers with faster feedback that supports informed decision-making during design iterations. Ultimately, the challenges of the study and further improvements are discussed.

Die-sinking EDM performance under the effect of tool designs and tool materials

Die-sinking EDM performance under the effect of tool designs and tool materials

A 3D Printing Platform for Design and Manufacturing of Multi-Functional Cementitious Construction Components and Its Validation for a Post-Tensioned Beam.

Three-dimensional printing of cementitious materials for construction has been extensively investigated in recent years, with several demonstration projects successfully carried out. These efforts aim to leverage the printing process to achieve more efficient production of components compared to conventional concrete technologies. This includes both the process itself (eliminating the formwork stage) and the flexibility in producing complexly shaped elements. To maximize the potential of 3D printing in the construction industry, additional steps must be taken, grounded in a holistic view of the entire process. This involves integration of the production chain, including design, materials, and manufacturing of components, to create elements with optimal performance, encompassing structural, environmental, and architectural aspects. Such multi-functionality requires the viewing of 3D printing not just as a production technology but as a platform enabling the integration of all these components. To advance this approach, quantitative tools are developed to optimize the following three key components: material composition; manufacturing parameters to ensure buildability; and design tools to optimize multiple performance criteria, particularly structural and architectural shape. A demonstration component, namely a post-tensioned beam, featuring two multi-functional characteristics-structural and architectural-is designed, produced, and evaluated. The scientific concepts and research tools used to develop these quantitative design tools are multidisciplinary, including rheological characterization, control of the internal structure and composition of granular materials, simulation of the mechanical behavior of green material during printing, and the hardened properties of the components, all utilizing structural optimization to enhance performance.

Research on the Design Method of Camellia oleifera Fruit Picking Machine

Camellia oleifera fruit pickers are essential for improving picking efficiency and promoting the Camellia oleifera industry. However, it is challenging to develop pickers that meet user needs. Current design tools and methods have limitations, such as a single model, poor synergy between integrated models, and subjective bias when analysing user requirements and translating them into product attributes. To solve these problems, this study proposes a new design decision model based on the Fuzzy Analytic Hierarchy Process (FAHP), Function Analysis System Technique (FAST), Theory of Inventive Problem Solving (TRIZ Theory), and extension transformation theory. The model was developed and applied to design an Camellia oleifera fruit picker. In this paper, an empirical investigation of an Camellia oleifera base in Wuhan was carried out, and multi-level demand analysis was used to identify the design demands in the behavioural process; FAHP was used to calculate the demand weights to clarify the design focus; expert knowledge was used to convert the demands into specific product functional features, and FAST was used to decompose these features to find the contradictory conflicts; TRIZ theory was used to determine the principles of resolving the contradictions, and the extension transformation theory were used to generate the creative design solutions for the products. By integrating FAHP, FAST, TRIZ theory and the extension transformation theory, the subjective bias in product design is eliminated, the design decision-making process is improved, and new methods and ideas are provided for the design of oleaginous tea fruit pickers and similar products. Finally, the conceptual design of an Camellia oleifera fruit picking machine was produced. However, the conceptual design has yet to be subjected to exhaustive simulation experiments and prototype testing. Future research will focus on conducting the necessary simulations, prototypes, and field tests to fully assess the feasibility and effectiveness of the design and make the required iterative improvements accordingly to commercialize the product eventually.

String-level compact modeling of erase operations in the body-floated vertical channel of 3D charge trapping flash memory

In this study, we examined the use of string-level compact modeling as an effective framework for circuit simulation focusing on erase operation in 3D charge trapping flash (CTF) memory devices. We analyzed the behaviors of the accumulated hole from p-type bulk (p-well) and the corresponding difference of channel electrostatic potential in the CTF cell string, which is attributed to the variation in hole barrier height in the channel of the ground select line (GSL) transistor during erase operation. We derived a formula for the hole current delivering positive potential from the p-well to the channel region and established a modeling procedure. Technology computer-aided design (TCAD) simulation results were used to extract model parameters and analyze channel electrostatic potential during the erase operation. Additionally, experimental data for erase speed were verified using simulation program with integrated circuit emphasis (SPICE) results. Because the erase efficiency is strongly related to hole behaviors based on the conditions of the GSL transistor, the proposed compact modeling is an effective tool for circuit designers and system architects to achieve better performance in erase execution and optimizing the design of 3D CTF memory devices.

Calorie and nutrient gap analysis of jail menu and nutritional status of the prisoners of District Jail, Faisalabad, Pakistan

Assessment of prisoners’ diet showed that it is deficient in calories as well as in Recommended Daily Allowance values. Caloric value was about 64.9% deficient considering one serving (plate per time) taken. A quantitative-qualitative mixed approach was adopted. Convenient purposive sampling was employed to recruit 112 participants. For quantitative assessment a descriptive cross-sectional study design and semi-structured data collection tool was used, and for qualitative evaluation, caloric and RDA value of different nutrients were calculated and in-depth interviews were conducted. Analysis showed that the diet provided was deficient in caloric and RDA values, thus the prison authority should design a proper strategy to ensure proper nutritional status of prisoners. Key words: Inmates, Prisoners, Servings, Associated factors, Mal-nutrition.

Sustainable Design of Pavements: Predicting Pavement Service Life

Pavement service life is an important factor that affects both the whole life cost and carbon footprint of a pavement. The service life of a pavement is affected by several different parameters which can be broadly classified into climate conditions, binder and mixture properties, pavement design, workmanship, and maintenance strategies. The current practice for determining service life of pavements involves the use of pavement design tools, which are used while constructing a new pavement or performing a reconstruction/resurfacing or pavement maintenance. In addition, field measurements using ground penetration radar, falling weight deflectometer, traffic speed deflectometer, and other techniques are also used to assess the condition of an existing pavement. The information from these measurements is then combined with pavement design software to predict potential pavement service life. The accuracy of the predicted pavement service life is affected by the associated uncertainties in the parameters that affect pavement life. The following paper presents various approaches that could be potentially used to determine the associated uncertainties in the estimation of pavement service life. The various uncertainty quantification techniques have been applied to a specific design, and the outcomes are discussed in this paper. The Monte Carlo simulation method, a system-level uncertainty quantification technique, can estimate a probabilistic pavement service life. The other uncertainty quantification schemes are software specific and provide probabilistic life factors by assumed statistical distributions. Hence, the Monte Carlo simulation technique could be one potential method that can be used for estimating a generalized pavement service utilizing predictions from various design software.

Determination of the load acting on the probe by separating force and torque during FSW of AA 6060 T66

AbstractThe development of suitable welding processes is required to meet the ever-increasing demands on joining processes, particularly for lightweight construction and increasing environmental awareness. Friction stir welding (FSW) represents a promising alternative to conventional fusion welding processes, particularly for the joining of low-melting-point materials such as aluminium and magnesium alloys, which present a number of challenges, including the formation of pores and the occurrence of hot cracks. The central element of the process is the friction stir welding tool, which consists of a shoulder and a probe. The rotation and the simultaneous application of pressure during the joining process create a friction-based heat input through the tool. The excellent mechanical properties resulting from dynamic recrystallisation during the welding process are a major advantage of the process. As a result, strengths comparable to those of the base material can be achieved. However, FSW is subject to process-specific challenges, including high process forces, which result in the fabrication of complex and robust devices. Additionally, high dynamic loads on the friction stir welding tools must be considered. In many cases, the design of friction stir welding tools is based on empirical data. However, these empirical values are machine-, component- and material-specific, which often results in under- or overmatching of friction stir welding tools. Sudden probe failure, component scrap, and low process reliability are the direct consequences of undermatching. Overmatching results in enlarged tools with limited accessibility, high heat input, and high process forces, leading to component deformation. The aim of this study is to determine the load on the probe by separating the forces and torque of the shoulder and the probe in order to be able to make statements about the load acting on the probe and the resulting stress state. The knowledge of the stress state can be employed to design friction stir welding tools, both statically and dynamically, for a specific welding task. A strategy was devised to distribute the load exerted on the shoulder and probe. To this end, the length of the probe was gradually reduced between the welding tests. The investigations were carried out with a force-controlled robotized welding setup in which AA 6060 T66 sheets with a thickness of 5 mm were welded. A Kistler multicomponent dynamometer type 9139AA allows to measure the Cartesian forces to be recorded in the x-, y-, and z-directions with a sampling rate of 80 kHz. The weld seam properties were determined by visual and metallographic inspections as well as tensile and bending tests in accordance with DIN EN ISO 25239–5.

Life cycle assessment of limestone calcined clay concrete: Potential for low-carbon 3D printing

Limestone calcined clay cement is being promoted throughout the construction industry as a way to considerably reduce the cement proportion in concrete mixtures. At the same time, concrete 3D printing could save resources by placing concrete material only where its functionalities are maximized. This study addresses the need for a quantification of the environmental impacts related to the material acquisition phase regarding a low-clinker 3D printing material.Compared to a 30 MPa 3D printing material from the literature, a 3D printable LC3-based concrete with low clinker content in the Quebec context displays a 36 % climate change score reduction with the same compressive strength (46 % reduction in the French context). A small impact shift is noticed in 6 out of 16 midpoint indicators (5 in the French case), mainly due to the calcined clay production. However, it is offset by the significant reduction in other indicators when considering an endpoint assessment. When considering different sourcing scenarios, a global warming potential variability of up to 15 % is observed.LC3 remains a viable solution for the mitigation of greenhouse gas emissions in the context of concrete 3D printing as it provides mechanical strength and enhanced structuration rate for low-clinker materials. However, a specific attention should be accorded to the calcined clay plant locations, especially when the calcined clay content is higher than the value used in this study. As a perspective, a mix design tool could allow the optimization of environmental impacts depending on expected fresh and hardened properties.

Numerical investigation of the geometric parameters effect of helical blades installed on horizontal geo heat exchanger

In this study, we investigate the enhancement of horizontal geothermal heat exchangers equipped with helical fins on the pipe's exterior and internally ribbed turbulators. Our approach focuses on the interplay between geometry and thermal efficiency through innovative design modifications. Utilizing the finite element method, three-dimensional numerical simulations assessed the effects of varying geometric parameters such as the diameter and thickness of the fins. Our findings indicate significant increases in heat transfer efficiency with the addition of helical fins; specifically, increasing the fin diameter from 5 mm to 10 mm results in a 15 % increase in the heat transfer rate, while doubling the fin thickness from 2 mm to 4 mm enhances the rate by 10 %. These improvements are due to the expanded surface area facilitating greater heat exchange. Optimization using the desirability function approach yielded models with high performance, achieving desirability scores of 0.9879 for outlet temperature and 0.9534 for the heat transfer coefficient. This reflects the effective tuning of geometric parameters to maximize thermal performance. The study also introduces two predictive mathematical models for the outlet temperature and convective heat transfer coefficient of the U-shaped pipe equipped with these enhancements. These models, derived from extensive numerical data, provide practical tools for future design and operational applications of geothermal heat exchangers. This research advances the design and operational efficiency of geothermal heat exchange systems, establishing new benchmarks for thermal efficiency in the field with actionable insights and robust mathematical tools.

Automatic Architecture Design for Distributed Quantum Computing

Abstract In distributed quantum computing (DQC), quantum hardware design mainly focuses on providing as many as possible high-quality inter-chip connections. Meanwhile, quantum software tries best to reduce the required number of remote quantum gates between chips. However, this "hardware first, software follows" methodology may not fully exploit the potential of DQC. Inspired by classical software-hardware co-design, this paper explores the design space of application-specific DQC architectures. More specifically, we propose AutoArch, an automated quantum chip network (QCN) structure design tool. With qubits grouping followed by a customized QCN design, AutoArch can generate a near-optimal DQC architecture suitable for target quantum algorithms. Experimental results show that the DQC architecture generated by AutoArch can outperform other general QCN architectures when executing target quantum algorithms.