Early Design Stage Research Articles

Fracture behaviour assessment of high-performance fibre-reinforced concrete at high strain rates using interpretable modelling approaches

High-performance fibre-reinforced concrete (HPFRC), a type of cementitious composite material known for its exceptional mechanical performance, has widespread applications in structures exposed to severe dynamic loading conditions. However, understanding nonlinear HPFRC fracture behaviour, particularly under high strain rates, remains challenging given the complexities of assessment procedures and cost-intensive nature of experiments. This study presents an interpretable framework for modelling and analysing HPFRC fracture strength at high strain rates. A wide range of machine learning methods, including ensemble techniques, were employed to capture multivariate effects of eight essential input features (e.g., mortar compressive strength, fibre physical and mechanical properties, cross-sectional area, and strain rate) on fracture strength response. To assess the derived models, a novel evaluation procedure was proposed involving a data-based analysis, employing established metrics (i.e., coefficient of determination, root mean squared error, and mean absolute error via K-fold cross-validation) and a domain experts-involved evaluation utilising global sensitivity analysis to discern first-order and higher-order interactions among input factors. The proposed approach efficiently yielded both quantitative and qualitative insights into crucial input factors governing HPFRC fracture strength with limited experimental data. The obtained findings highlight the significance of multivariate effects, such as the interaction between strain rate and fibre tensile strength, and between fibre volume and fibre diameter, on fracture behaviour. The proposed interpretable framework aims to provide a powerful tool for proactive material failure analysis by understanding fracture behaviour and identifying potential weak and strong interactions among input factors of HPFRC-based samples. Moreover, the utilisation of the proposed approach enables researchers and civil engineers to efficiently focus on the most critical input parameters during the early design stage and ensuring the structural integrity and safety of HPFRC-based constructions.

Energy Flow Modeling and Low Carbon Analysis of Processoriented Production Line

Nowadays, energy and environmental problems are becoming more and more prominent, and the energy-based manufacturing industry also needs to face huge energy consumption problems. Therefore, production processes need to be investigated from an energy efficiency perspective at an early design stage so that maximum benefits can be obtained at low cost, low time and low risk. This process industry carbon emissions system does not calculate the consumption of raw materials and electricity in a generalized way at the boundaries of the plant. Rather, we delve into each step of the process and perform mathematical modeling of the process through a graphical approach, incorporating each link and factor that constitutes and influences the change in carbon emissions, and performing an aggregation of carbon emissions for the entire production line. It facilitates the subsequent improvement and optimization of the entire process-based production line, providing the basis for implementing and establishing the best energy-saving system.

Using building information modelling to optimise design quality of natural lighting in Iraqi school buildings

Abstract One of the most important aspects directly affecting a building’s performance is the design quality of the natural day-lighting. The present research evaluates the effectiveness of daylight in school buildings in the city of Baghdad. It aims to ascertain how the characteristics of building windows affect the quality of day-lighting, the objective of this exercise being to improve educational processes in schools by making improvements in relation to the aspect of provision of satisfactory day-lighting levels. A building simulation approach was adopted, and using Autodesk Revit 2018 (Insight plug-in), a daylight analysis was conducted under the aegis of the Building Information Modelling (BIM) technology. For the case study, we selected the modern design of a 24-classrooms school building, which is part of a plan for the construction of 1,000 Iraqi schools. Leadership in Environmental and Energy Design (LEED v4) simulation was conducted on the school building design to provide the results, which were measured and analysed according to LEED v4 standards. The results demonstrated that opening dimensions and direction have an essential impact on natural day-lighting. The research concluded that using BIM simulation tools enhances the design quality of the natural lighting in such a way that compliance with LEED v4 requirements is achieved. The present study will assist in identification and assessment of the problems concerned with natural lighting levels in Iraqi school buildings as well as resolving these at an early design stage.

Development of a voxel-based CAD system for upstream life cycle design and its application to LCA

The comprehensive design of product life cycles is a promising approach to realizing circular manufacturing systems. However, design support systems that can be used for the life cycle design during upstream stages have not been established. The purpose of this study is to develop an easy-to-use CAD system for the early stages of product life cycle design. The system supports creating a product model in voxel format and a life cycle process model as a process network in an integrated manner. The voxel models make it easy to create the rough shape of products and quantify the mass of each component for LCA. An LCD monitor's life cycle was designed using the proposed CAD tool. Fourteen design candidates were created and LCA was performed to compare the designs from the perspective of life cycle environmental impact. Through the case study, we confirmed that the tool is a practical system that can quantitatively examine what design improvements contributed to the reduction of environmental impact at an early design stage before creating detailed 3D-CAD models.

Integrating Variety, Assembly, and Lightweight Design in Product Architecture of Cabin Monument

In aircraft cabin design, particularly in the design of aircraft monuments such as galleys, there are specific conflicting objectives that needs to be balanced. The key objectives are to meet customer requirements, optimise assembly processes for increased production and achieve lightweight design for efficient operations. The paper presents a methodical approach that integrates variety, assembly efficiency and lightweight design considerations into the early stages of product architecture design. Using the after monument of the Airbus A320 family as an example, the paper proposes specific module drivers such as ‘common unit’, ‘different specification’ and ‘load path compatible modules’ to guide the design process. These module drivers aim to harmonise the different objectives of stakeholders, including aircraft manufacturers, cabin manufacturers and airlines. The method is illustrated using a geometric representation of product architecture alternatives optimised for each of the variety, assembly and lightweighting objectives. Overall, the paper contributes to the advancement of a stakeholder-oriented modularisation approach and provides a starting point for future developments. It highlights the importance of defining module drivers and visualising the trade-offs to achieve a balanced product architecture in the aviation industry.

Ecodesign approach for complex systems – Electric Vehicle case study

The reduction of environmental impacts from the early stages of product and system design is a major challenge. Thus, traditional ecodesign methods are not appropriate because they reason by fixing the functional definition of products/systems and address the complexity of these new systems in a simplistic way, which leads to a marginal reduction of impacts. Moreover, these methods do not consider the attractiveness for users, leading to an optimization oriented towards functional reduction. In this paper, a comprehensive ecodesign approach is presented suggesting a paradigm shift, which involves a hybrid ecodesign approach based on constraint modeling of systems based on the constraint satisfaction problem formalism (CSP) with more traditional environmental assessment methods to decide which design choices to make. This approach was applied to generate and characterize product and life cycle architectures of a serial hybrid vehicle powertrain focusing on the use and end-of-life phase.

ІМІТАЦІЙНЕ МОДЕЛЮВАННЯ СКЛАДНИХ СИСТЕМ

The article examines simulation modeling of complex systems. Simulation modeling offers a means to replicate real-world processes in a controlled virtual environment, providing insights into system dynamics, interactions, and behaviors that may be difficult or impractical to observe directly. With the rapid advancement of computational technologies, simulation models have become increasingly sophisticated, allowing for more accurate and detailed representations of complex systems. In today's interconnected and rapidly changing world, decision-makers in business, healthcare, finance, environmental science, and many other sectors rely on simulation modeling to analyze scenarios, evaluate strategies, and make informed decisions. It serves as a valuable tool for forecasting outcomes, optimizing processes, and identifying potential risks and opportunities. Moreover, simulation modeling plays a crucial role in innovation and research by providing a platform for hypothesis testing, data analysis, and validation of theoretical concepts. It facilitates the exploration of new ideas, designs, and strategies in a risk-free virtual environment before implementation in the real world. Additionally, the educational significance of simulation modeling cannot be overstated. It offers students and researchers a hands-on approach to learning complex concepts, enhancing their understanding and practical skills in various disciplines. By engaging with simulation models, learners can develop critical thinking, problem-solving, and analytical skills essential for success in today's competitive landscape. It is noted that simulation modeling, in particular machine simulation, is widely used in the study of complex systems due to a number of advantages. These advantages include the ability to quickly obtain information at the early stages of the preliminary design of systems, the study of conditions that are not amenable to full-scale experiments, the prediction of system behavior, the reduction of time for testing simulation models, the acquisition of a large amount of information about real processes, and the solution of optimization tasks of economic systems. This approach makes it possible to solve tasks that are often impossible or very difficult to perform with the help of other research methods.

Inductance Analysis of Transverse Flux Linear Synchronous Motor for Maglev Trains Considering Three-Dimensional Operating Conditions

Despite the availability of powerful finite element method (FEM) tools, an accurate and fast analysis method is needed in the early design stage to find an initial motor design structure with the expected performance. When it is applied to maglev trains, inductance of the transverse flux linear synchronous motor (TFLSM) is affected by the cogging structure, the change of air gap length and the variation of lateral offset. Therefore, the analytical method of inductance in the TFLSM is different compared to that in the conventional rotating field winding motors. The three-dimensional (3-D) analysis models for obtaining inductance parameters are comprehensively introduced in this paper. The results of the inductance analytical calculation are verified and evaluated in light of the FEM simulation results and the prototype measurements.

Probabilistic evaluation of dynamic positioning operability with a Quasi-Monte Carlo approach

During the design phase of an offshore unit, estimating the station-keeping capabilities of the dynamic positioning (DP) system is mandatory. This means, in conventional offshore applications, to determine the maximum sustainable wind speed as a function of the encounter heading, which the unit may counteract by employing the onboard actuators or mooring lines only. Besides the deterministic estimation of DP capability, it is possible to assess the operability of the DP system following a non-deterministic probabilistic process by employing the site-specific joint wind-wave distributions to model the environment. In such a case, the operability results from a Monte Carlo integration process. Here it is proposed to enhance the applicability of the probabilistic analysis of DP operability, investigating the application of a Quasi-Monte Carlo method. In this sense, the procedure uses quasi-random samplings following a Sobol sequence instead of employing random samples of the joint distributions. In this paper, the Quasi-Monte Carlo process is tested and compared on a reference ship, highlighting the improvements to the established probabilistic DP prediction concerning the number of calculations needed to estimate operability. The significant reduction of computational time makes the newly implemented method suitable for the early design stage applications.

Early-Stage Design Parameters for Low-Energy Solar Rural Houses in Qinghai-Tibet Plateau

As an effective solution to achieve carbon neutrality, the development of low-energy solar houses is now in the ascendant in China. The Qinghai Tibet Plateau (QTP), which is rich in solar energy resources, is considered to be an important region for solar technologies studying. Rural houses are the main living form for residents in the QTP, early-stage design parameters play an important role in achieving low-energy consumption of rural houses. However, researches on the design method and parameters of low-energy solar rural house were relatively poor at present. This paper examined the influence of early-stage design parameters on the energy performance of low-energy solar rural houses incorporating building-integrated photovoltaic (BIPV) roof systems. A 2-storey 110 m2 rural house was studied as an archetype. Through simulation, a range of related building parameters, such as shape coefficient, window-to-wall ratio (WWR) and azimuth angle were analysed and compared. The impact of early-stage design parameters on energy consumption in winter, ventilation in summer and BIPV system annual energy yield were considered.

Sensitivity Analysis and Multi-Objective Optimization of Skylight Design in the Early Design Stage

Building geometry design decisions are important for energy efficiency and daylight performance. Sensitivity analysis, coupled with optimization, is an important approach to investigate and optimize building geometry in the early design stage. Incorporating skylights is an important daylighting strategy in commercial buildings; however, skylight-to-floor ratio (SFR) is often the only design variable evaluated in precedent studies. More design variables related to skylight geometry, clerestory geometry, skylight material, and building geometry need to be evaluated. This study investigates the skylight design of a 2000-square-meter commercial building. Eighteen design variables are evaluated according to their influence on building energy and daylight performance. One-at-a-time (OAT), linear regression, and Morris sensitivity analysis approaches are utilized to identify the most influential variables. Seven of the twelve building geometry variables and two of the six building material variables are considered as important. Then, a multi-objective optimization with genetic algorithms is processed to find out the optimal design solution. The three objectives are energy use intensity (EUI), daylight autonomy (DA), and daylight uniformity (DU). After the optimization, five candidate design options are picked from the Pareto front. Discussions are made on the features of these designs, and one design is selected as the optimal solution.

Calculation and Selection of Airfoil for Flapping-Wing Aircraft Based on Integral Boundary Layer Equations

The flight of a migratory bird-like flapping-wing aircraft is characterized by a low Reynolds number and unsteadiness. The selection of airfoil profiles is critical to designing an efficient flapping-wing aircraft. To choose the suitable airfoil for various wing sections, it is necessary to calculate the aerodynamic forces of the unsteady two-dimensional airfoil with a Reynolds number in the range of 105. While accurate, calculating this by solving the Navier–Stokes equations is impractical for early design stages due to its high consumption of computing resources and time. The computational demands for extending it to 3D aerodynamic calculations are even more prohibitive. In this paper, a relatively simple method is proposed. The two-dimensional unsteady panel method is utilized to derive the inviscid flow field, the unsteady integral boundary layer method is utilized to solve the boundary layer viscous flow, and the eN transition model is adopted to predict the position of the transition. These models are coupled with the semi-inverse interaction method to solve the aerodynamics of the unsteady low-Reynolds-number two-dimensional airfoil. The unsteady aerodynamics of the symmetric and cambered airfoils at different wing sections are calculated respectively by the proposed method. Mechanism analysis of the calculation results is conducted, and a symmetrical airfoil or a slightly cambered airfoil is recommended for the wing tip, a moderately cambered airfoil is suggested for the outer-wing section, and a highly cambered airfoil is suggested for the inner-wing section.

S-FMECA: A Novel Tool for Sustainable Product Design - Additive Manufacturing

The choices made in the early design stage (EDS) will largely define the environmental impacts of a product. The purpose of this paper is to develop an eco-design method used for assessing semi-quantitatively the sustainability of an additively manufactured product since the EDS. This article presents a semi-quantitative method to support EDS-conscious environmental decisions. A novel Sustainable-Failure Mode, Effect, and Criticality Analysis (S-FMECA) tool is developed to support designers in the conceptual design phase, to guide the choices, and to provide a valuable evaluation of the future additively manufactured product. Through the integration of the environmental aspects in FMECA analysis, systematic prevention of errors, and enhancement of sustainability since the EDS would be the main advantage of this tool.

Efficiency Analysis of the Photovoltaic Shading and Vertical Farming System by Employing the Artificial Neural Network (ANN) Method

Productive facades, consisting of photovoltaic shading and vertical farming systems, have been proposed as a means to improve the thermal and visual status of residential buildings while also maintaining energy performance and providing vegetables. However, how to quickly and accurately predict electricity and vegetable output during the numerous influencing architectural and environmental factors is one of the key issues in the early stages of design, and few studies have investigated the impact of such structures on both indoor environmental qualities and production performance. In this paper, we present a novel prediction method that uses experimental data to train and test an artificial neural network (ANN). The results indicated that using the Bipolar Sigmoid activation function to process the experimental data input to the artificial neuron network gives more accurate predicted results both in the yield of photovoltaic shading and vertical farming systems. In addition, this prediction method was applied to a typical high-rise residential building in Singapore to assess the self-sufficiency potential of high-rise residential buildings integrated with productive facades. The results indicated that the upper part of the building can meet 20.0–23.1% of the annual household electricity demand of a family of four in a four-room residential unit in Singapore and almost the entire year’s vegetable demand, while the middle part can meet 18.4–21.2% and 89.1%, respectively. The results demonstrated the importance of a productive facade in reducing energy demand, enhancing food security, and improving indoor visual and thermal comfort.

Machine Learning Application for Early Power Analysis Accuracy Improvement: A Case Study for Nets Switching Power

In the quest for precise power estimation during the early phases of design, the absence of a Standard Parasitic Exchange File (SPEF) with interconnect R/C values poses a significant hurdle. To address this challenge, we introduce a Machine Learning (ML) approach designed to predict net power metrics at the Gate Level without relying on SPEF. Net features are extracted from Electronic Design Automation (EDA) tools, facilitating the training of models for the prediction of Net Switching Power. Notably, the Random Forest model emerges as the most effective, achieving high accuracy by reducing power error from around 20% to a mere 0.1%.Furthermore, our innovative approach enhances efficiency by bypassing the traditional SPEF generation process. This results in a significant 5x reduction in runtime compared to the conventional flow, with a notable decrease from 163.6 minutes to just 33.7 minutes. This substantial acceleration is achieved by skipping the time-intensive synthesis and physical design steps required for SPEF.In summary, our ML-based methodology not only achieves swift and accurate power estimation in the early stages of design but also liberates the process from the constraints of SPEF dependency. This marks a transformative shift in the landscape of power estimation methodologies.

Developing a Performance-Based Approach to the Effect of Roof Features on Fire Safety in Buildings with Atriums

Developing technology, changing social structures, and the threat of resource depletion have changed the design of buildings. Therefore, design approaches are developed to increase user comfort and reduce energy consumption by utilizing natural ventilation and lighting. The design of the distribution of outdoor air and light into the spaces through vertical and horizontal gaps reduces the energy demand of the mechanical systems and increases occupant comfort. The atrium is the preferred vertical gap in modern buildings for distributing natural air and light to interior spaces under appropriate conditions. However, in buildings with atrium, there is a risk of fire spreading in the event of a fire due to the uninterrupted gaps between the rooms. It is necessary to ensure the operability of the design by monitoring the measures to be taken in the early stages of design using performance-based fire safety methods. This study develops design strategies for fire analysis in atrium buildings using computational fluid dynamics (CFD) simulation technology. Atrium height, roof type, and slope characteristics are analyzed for the stack effect, which is the main factor in the movement of smoke and flames. As a result of the numerical analyses consisting of flat, unidirectional, and bidirectional sloping roof type, 10, 20, 30-degree roof slope, and one-meter rising atrium roof variables, the effect degrees for smoke dispersal and temperature control are investigated. Fire Dynamic Simulator, which uses CFD capabilities, and Smokeview software, which can visualize the results, were used for the numerical analysis. Correlation analysis was used to determine the effect of variables on temperature. The results showed that flat roofs and designs with increasing height were effective in delaying the spread of smoke and increasing the stack effect in the atrium, while the contribution of roof slope to fire safety was weak.

Modelling the energy demand of a residential building using an artificial neural network (ANN) approach

The consumption of fossil fuels accelerates and accentuates the formation and development of the climate change phenomenon. Understanding the energy demand in the early-stage design could lead to more energy savings. There are several methods for predicting buildings energy demand and they differ overall in terms of prediction quality. The multicollinearity of variables, linearity, and other conditions limit the predictive quality of standard models such as linear regression modeling. This paper is interested in developing an energy demand prediction tool based on artificial neural network modeling ANN to test its limitations and its predictive quality. For this purpose, and based on the scientific literature, a panel of parameters often used by architects at the time of architectural design was selected, which are, the thermal resistance of the external walls, the type, and rate of glazing, the orientation, the shading devices the set point cooling PMV and natural ventilation rate schedule. A campaign of 600 dynamic thermal simulations is then run under energy plus using the Latin Hypercube Sampling (LHS) approach. The best ANN model obtained after testing several activating functions gave a prediction potential of over 99.7%. The model also ranks each parameter according to its importance in the equation identifying the energy demand. It can therefore be assumed that the artificial neural network technique is effective and the ANN outperforms the other prediction methods.

Integrating building services in the early stages of Architectural design: A primer for Architects and Architectural students

This paper delves into the pivotal role of building services in the initial stages of architectural design, with a primary focus on architects and architecture students. Recognizing the critical interplay between architectural design and building services, this study aims to guide professionals and aspiring architects toward the development of functional and sustainable buildings. In the contemporary architectural landscape, the integration of building services is increasingly recognized as fundamental to the success of any construction project. As buildings become more complex and sustainability gains precedence, architects must adeptly incorporate building services considerations from the project's inception. The paper begins by highlighting the key building services involved in the early stages of design, encompassing mechanical, electrical, plumbing, and other essential systems. Emphasis is placed on the synergy between architectural aesthetics and the seamless integration of these services, promoting an interdisciplinary approach that optimizes both form and function. The educational dimension of this paper is tailored to architecture students, addressing the gap in curricula that often neglects the intricate relationship between architectural design and building services. By incorporating real-world examples and interactive learning exercises, the paper seeks to empower the next generation of architects with the knowledge and skills needed to create spaces that are not only visually compelling but also functionally efficient and environmentally sustainable. Ultimately, this paper advocates for a paradigm shift in the architectural design process, where building services are not treated as mere appendages but as integral components shaping the very essence of architectural creations. Through a collaborative and informed approach, architects and students alike can contribute to the realization of buildings that stand as testaments to both visionary design and responsible, sustainable construction practices. Key Words: Building services, architectural design, HVAC, lighting, plumbing, fire safety.

Equations for early-stage design embodied carbon estimation for concrete floors of varying loading and strength

Building designers are increasingly tasked with tracking and reducing the embodied carbon of structural systems, including concrete floor slabs. Design decisions that affect both the structural performance and the embodied carbon of a concrete floor are often made in the early design stages without complete structural analyses and life cycle assessments, which are commonly finalized in later stages. In this study, we employ polynomial regression models to estimate the embodied carbon for ten concrete floors to guide practitioners toward environmentally-informed design decisions. Six reinforced concrete (RC) and four post-tensioned (PT) floors are evaluated with varying five structural parameters including the span length, concrete strength, live and dead loads, and deflection limit, equating to 41,328 design scenarios for the RC systems and 27,552 design scenarios for the PT systems, while also considering 32,440 different concrete mix Environmental Product Declarations. The analytical equations are deployed in a web-app intended for practitioners to quickly evaluate and compare different design scenarios and floors. By estimating the embodied carbon of concrete floors while accounting for design and material uncertainty, these equations are ready for immediate use to reduce the carbon footprint of future buildings.

Integrated climate-responsive thermal load ML model and cost/embodied energy estimate from a preliminary building design

This paper proposes a framework applicable to preliminary building design that cohesively integrates material cost estimation, embodied energy assessment, and the prediction of the thermal demand of a building. At its core is a climate-responsive machine learning model specifically developed using readily accessible early-stage design data. This model forecasts the thermal demand both in the present and in the face of likely climatic shifts. To strike a balance in energy performance goals, material cost estimation is seamlessly incorporated. Building on the gradient boosting machine, the proposed approach showed better performance than other algorithms, predicting thermal demand 25 to 37.5 times faster than conventional energy software without compromising accuracy. The key findings of the presented case studies highlight the significant influence of the Window-to-Wall ratio and floor thermal mass on thermal demand—especially in mitigating cooling demands posed by potential climate change scenarios. By enhancing interior floor types, heating demand can be reduced by nearly 49%. The study further suggests that incorporating climate change considerations into life cycle energy designs is projected to amplify thermal and cooling loads by 32% and 94%, respectively, highlighting the crucial role of early design decisions in shaping sustainable and efficient buildings.