Early Design Stage Research Articles

A comparison of 4th and 5th generation thermal networks with energy hub

State-of-the-art thermal networks are key to address decarbonization of the heating and cooling in buildings. Energy Hub considers synergy between elements and allows rapid comparison between district energy systems incorporating 4th generation (4G) and 5th generation (5G) thermal networks at early-stage designs. To better understand and quantify the differences between systems based on 4G and 5G, a mixed-integer linear model distinguishing specific features of both technologies is implemented. We find systems relying on 5G to perform environmentally and financially better than with 4G generation over a wide set of scenarios. This is due to the warm/cold coupling characterizing 5G technologies. Systems based on 5G can also reduce its carbon emissions more than those with 4G. However, performances with both technologies appear sensitive to the topology and location of central energy station.

Integrated Workflow Development for Data-Driven Neighborhood-Scale Building Performance Simulation

Abstract As urbanization intensifies, cities are key contributors to energy consumption and carbon emissions, accounting for a significant portion of global energy use and CO2 emissions. This paper introduces a systematic approach to support the development of urban projects with minimized operational carbon footprints through the integration of data-driven building performance simulation (BPS) tools in early-stage design. Emphasizing the necessity for a collaborative effort among designers, policymakers, and other stakeholders, we discuss the evolution of BPS toward incorporating data-driven tools for energy need reduction and informed decision-making. Despite the proliferation of modeling methods and data-related challenges, we present a theoretical workflow, supported by interactions with design firms in the US and European Union (EU) through interviews. This structured approach, demonstrating adaptability and scalability across urban contexts, foregrounds the potential for future data-driven integration in design practices. Grounded in theoretical concepts and preliminary real-world insights, our work emphasizes the transformation of standard activities toward data-driven processes, showcasing the crucial role of practical experience in advancing sustainable, low-carbon urban development.

Going beyond carbon: Influence of structural parameters on the environmental impacts of typical building structures

The construction sector has a large impact on the environment, be it climate change, biodiversity loss or resources depletion. Part of those impacts comes from construction materials, and more specifically, concrete and steel building structures. Early-stage design tools including environmental damage assessment are therefore required to achieve sustainable construction practices. This article presents a parametric approach aiming at studying the influence of early-stage structural parameters on the environmental impacts of a structure. The developed methodology combines parametric structural design with a multicriteria cradle-to-grave Life Cycle Assessment, applied to typical housing structures such as grid based beam column structures with a central core for lateral stability. Results show that span, number of levels and materials greatly influence the environmental impacts of considered structures. Climate change scores per floor area range from 80 kgCO2/m2 for short span timber structure to 215 kgCO2/m2 for long span steel buildings with few levels. Beams and slabs have the largest contribution in such impacts. Changing the production processes of materials is a way to reduce greenhouse gas emissions. However, decarbonizing steel production processes or reducing the cement content of concrete participate in burdenshifting with increased scores in several impact categories, such as carcinogenic toxicity or ozone depletion.

Numerical investigation of vibratory response of planetary gear sets and modulation effects induced by carrier rotation

In geared system, the main source of excitation is generated by the mesh process itself. Depending on the dynamic conditions involved, the system may present a variety of problems, ranging from acoustic nuisance to system failure. Predicting and controlling the mesh process at an early design stage is a key point to avoid such issues. The problem is complex, mainly due to its multi-scale nature. Indeed, the vibroacoustic behavior of geared systems (on the scale of a meter) depend on the local micro-geometry of the teeth (of the scale of a micron), associated with the transmission error. Moreover, the problem is parametric in nature, due to the periodic fluctuation of the mesh stiffness. These parametric internal excitations generate dynamic mesh forces which are transmitted to the housing through wheel bodies, shafts and bearings. In the case of planetary gear sets, the numerical prediction presents a complementary challenge as, in many application, the carrier rotation modulates the housing vibration response, at its rotational frequency. This paper present an original simulation process to deal with modulation effects in planetary gear systems.

NVH multiobjective and robust optimization of electric motor design, including power losses

As part of electric motor design, compromises between NVH considerations, electrotechnical design and performance targets must be made. Electromagnetic excitation within the airgap results in structure-borne and airborne noise transmission to the vehicle. High-frequency tones are characteristic of this noise. Efficient computation methods exist to estimate these vibration and noise levels. From the early design stage, NVH performance can be diagnosed and optimized. The focus on NVH can no longer be separated from efficiency optimization. Simulation workflows and optimization loops, developed for NVH-related targets, are updated to consider copper and iron losses. The modification of the geometrical design must be addressed to reduce both dynamic excitation and energy loss, or at least offer compromises. Electric motor design optimization requires the handling of multiple objectives and constraints from multiple physics. In addition, design robustness must be ensured as part of the industrial process: manufacturing tolerances have to be considered to define a robust optimized design.

A procedure for estimating mitigation effect of air injection on ship underwater radiated noise

Increasing evidence exists that underwater radiated noise (URN) from ships has a negative impact on marine ecosystems. This has led to numerous initiatives aimed at monitoring and mitigating ambient sound levels generated by shipping. Within the European Union, noise abatement is being addressed by a wide range of stakeholders, with activities focused on developing procedures and thresholds for evaluating noise impact as well as knowledge on the effectiveness of a range of mitigation measures. In the SATURN project, MARIN is studying air injection systems for mitigation of both propeller cavitation and machinery noise; the so-called "Prairie" and "Masker" systems. Model-scale tests have been performed in order to quantify performance in terms of sound level reductions for a range of test conditions. In this paper we elaborate a procedure for applying the processed measurement data to ship-scale URN predictions for assessing mitigation potential during the early design stage. Data reduction of the model test results is explained, leading to a model for estimating the mitigation effect and power requirement of the two systems. We subsequently apply the model to a number of test cases, covering both measured and calculated source levels. Limitations of the approach will also be discussed.

Prediction of Fan Array Performance with Polynomial and Support Vector Regression Models

The increasing utilisation of demand-controlled ventilation strategies leads to the frequent operation of fans under part-load conditions. To accurately predict the energy demand of a ventilation system with a fan array in the early design stages, models that calculate reliable results across the whole operating range are required. We present the comparison of a polynomial and a machine learning approach through support vector regression (SVR) to predict the fan performance over a wide range of typical operating points. For fitting and validation, we use experimental data. We investigate the extrapolation performance of both approaches. The SVR model achieves a slightly better representation of the experimental data with a lower error, especially when only sparse data are available. Both approaches yield similar results when the evaluation is conducted within the experimentally captured domain but deviates outside the domain. At operating points that are far from the experimentally captured domain, the polynomial models yield fan efficiencies that are physically plausible, while the SVR models drastically overpredict the fan efficiency. To rate the influence of such deviations towards modelling the actual energy demand, both approaches are applied to an operation simulation of a simplified office building. Both approaches yield similar results despite differing extrapolation capabilities.

Implementing resilience in sustainable building design: Testing selected resilience criteria in a case study

Abstract Climate change is causing unprecedented changes in precipitation, extreme temperatures, and weather-related threats. Without effective intervention, these changes are expected to escalate in the coming years, potentially causing substantial damage to buildings. Paradoxically, the buildings themselves possess the potential to both exacerbate and alleviate climate change. To achieve a balance, the design of the building must adhere to minimal sustainability standards, taking into account resilience. Popular building rating systems, currently skewed towards sustainability, often neglect resilience principles. This paper aims to assess five selected resilience criteria from a new module incorporated into an existing building certification system, SBToolCZ. Validation occurs through a multi-residential building case study in Prague, evaluating the effectiveness of the criteria in terms of feasibility, accuracy, consistency, and time/data requirements. The results of this criteria test determine the clarity, achievability, and informativeness of the selected criteria. If gathering information and meeting benchmarks within a specific time frame proves challenging, adjustments to the criteria may be necessary for attainability and specificity. Integrating resilience features into sustainability rating systems, typically used in the early stages of design, can encourage designers to incorporate resilience into their projects. This proactive approach could lead to long-term reductions in environmental, social, and economic impacts, especially during weather-related hazards.

Analysis of carbon emission characteristics and establishment of prediction models for residential and office buildings in China

Intensifying environmental issues make carbon reduction crucial, especially in the construction industry. Embodied carbon emissions (ECE) in buildings characteristically involve concentrated, intensive emissions over a short period. Implementing energy-saving and carbon-reducing technologies has raised ECE rates. Therefore, analyzing carbon emission (CE) characteristics during the production and construction phases is necessary. Currently, essential data on ECE is lacking, the calculation methods are complex, and ECE's characteristics and influential factors in residential and office buildings remain unclear. Moreover, there is a lack of prediction models for ECE in the construction phase that apply to current construction methods and CE factors. The research on prediction models for ECE has primarily focused on the production phase but has paid little attention to the contribution of non-structural materials to embodied carbon (EC). In this study, reinforced concrete residential and office buildings in hot summer and cold winter regions (located in the southeast part of mainland China, with a subtropical monsoon climate) were considered typical types, and the CE characteristics of two main phases, the production and construction phases, were analyzed. Based on critical features, such as the number of floors and the basement, prediction models for ECE in residential and office buildings were established at the design stage. The models were validated with mean absolute percentage error (MAPE) values below 4%, and coefficient of variation (root mean square error) (CV [RMSE]) values less than 0.02. The prediction models enabled a rapid estimation of ECE, encouraging designers to consider EC in the early design stages.

An Infrastructure’s emissions assessment tool: AMICO - Account Method of Infrastructures embodied CarbOn

Abstract In 2013, the UK Infrastructure Carbon Review report highlighted how the infrastructure sector contributed to the national total emissions and, therefore, had a key role in contributing to the national reduction, adopting targeted strategies to reduce them. The main objective of AMICO’s research project: “Account Method of Infrastructures embodied CarbOn”, is the development of a tool for the analysis and parametric evaluation of carbon emissions (Embodied Carbon, EC) and energy resource requirements (Embodied Energy, EE) related to the design and construction of infrastructures (railway tunnels, bridges, highways …) AMICO tool is designed to be used for tendering, contract start-up and construction of infrastructure by Webuild Group (Italian Joint Stock Company) as part of a research project funded by MIMIT - Ministry of Business and Made in Italy, with the scientific support of the Politecnico di Torino (Department of Architecture and Design). AMICO aims to assess potential impacts in terms of greenhouse gas emissions (GHG) and potential primary energy resource needs over the infrastructure life cycle and at different design process stages, allowing designers and constructors to evaluate decarbonisation in their choices. AMICO is currently organised into two calculation models: 1. An Excel spreadsheet file, divided into worksheets containing algorithms for accounting of EC and EE, organised into two usage models: Basic, which will be used for tenders and the early design stage, and Advanced, which will be used in the detailed design or construction stage. 2. A code for the BIM environment that accounts for impacts (EE and EC) related to infrastructure systems (as defined by UNICLASS) combined and concurrently with design in the BIM environment (this section is not the focus of this paper). An innovation aspect is sharing a database of environmental data about materials and processes (transportation, construction, demolition) directly related to the Bill of Quantity without using Life Cycle Assessment software. The database will be integrated into the Datalake to speed up and optimise infrastructure design and construction work. You can set the database to the location of the work. Through data digitisation, AMICO is designed to be as automated, easy to fill in, and intuitive as possible.

A novel method of high-density urban block form generation based on multi-objective solar performance optimization: A case study of Nanjing

Efficiently harnessing solar radiation in high-density urban environments to optimize block forms holds significant potential for advancing urban low-carbon development. This paper introduces a novel method for generating high-density block forms based on comprehensive solar utilization. Grasshopper was used to compile an automated workflow for block form generation, solar simulation, and multi-objective optimization algorithms. Leveraging maximum solar radiation within and surrounding the block and effective photovoltaic utilization ratio are the three objectives in the multi-objective optimization process. From 60,000 generated samples, 1200 block forms were selected as the Pareto fronts. As the plot ratio of the generated blocks increases from 3 to 7, the annual mean solar radiation around the block decreases from 891.3 kWh/m2 to 863.5 kWh/m2, a reduction of only 3.1 %. When the number of blocks within the block increases from 1 to 4, the average solar radiation inside the block increases from 484.8 kWh/m2 to 516.1 kWh/m2, an increase of about 6.5 %. Additionally, as site coverage increased from 30 % to 60 %, the average solar radiation inside the block decreased from 549.2 kWh/m2 to 459.8 kWh/m2, a decrease of approximately 17 %. Notably, the photovoltaic power generation fell from 90.3 kWh/m2 to 61.2 kWh/m2, a decrease of about 32 %. The results indicate that the grid modeling method and multi-objective optimization algorithm adopted in this study can significantly improve the diversity of block morphology and achieve multiple solar performance requirements. The findings support designers in swiftly assessing building volume rationality and solar utilization potential at the early stages of urban design.

Enhanced fatigue SN curve generation for gear bending fatigue life prediction using multi-objective optimization algorithms

This study presents a comprehensive methodology for fatigue life prediction and SN curve generation in gear systems, integrating specimen and product-level testing, simulations, and multi-objective optimization. The approach addresses the discrepancy between specimen-based testing and actual product performance through a novel multi-fidelity framework. To solve the proposed multi-objective optimization problem, several optimization algorithms were compared and analyzed, demonstrating the effectiveness of each approach in balancing different objectives. A key innovation is the development of an empirical formulation-based surrogate model, enabling efficient optimization while significantly reducing computational demands. The methodology covers both specimen and product-level testing along with simulations, offering a holistic solution for accurate fatigue life prediction. Extensive validation through experiments and comparison with commercial software confirms the reliability and practical applicability of the method. This approach provides a sustainable and efficient solution for SN curve generation and fatigue life prediction in automotive gear train development, allowing for continuous improvement as new data becomes available. Its practicality and accuracy make it particularly valuable for early-stage design optimization and large-scale applications in the automotive industry.

Early-stage design support for ice-shell architecture with integrated snow drift simulation tool

The data on snow distribution on roofs can aid in optimizing the weatherability of ice-shell architecture. However, there is currently a lack of technology for predicting snow drift in early-stage design. The study proposes a snow drift simulation method for ice shells, utilizing Butterfly to call the OpenFOAM solver. A snow drift simulation tool based on this method was developed and validated through three case studies. The validation results provide suggestions for mesh accuracy and time steps for typical ice-shell architecture simulation with the tool. Simulation results from the tool are used to analyze the impact of ice-shell architectural specific features (texture, plane, orientation) on weatherability, focusing on the coupling optimization of snow and radiation for typical ice shells. The study demonstrates the tool's effectiveness, simplifying the simulation process and extending its applicability to other cold-region buildings. The feature analysis indicates that the ribbed texture more effectively blocks solar radiation through snow distribution.

Machine learning-based surrogate models for fast impact assessment of a new building on urban local microclimate at design stage

The rapid urbanization introduces changes in the local urban microclimate. Many efforts have been paid on the impact assessment of building design on local microclimate. However, there is still a lack of efficient and accurate prediction method for assessing the impacts on local microclimate when making the design of individual buildings. Two complementary machine learning-based surrogate models are proposed, including an SVR-based local air temperature model and a LightGBM-based local wind velocity model. They are identified by evaluating and comparing eight alternative machine learning models, four for each model development. 200 sets of CFD simulation data corresponding to different building designs are used for the model training and validation. The results show that the developed surrogate models can dramatically reduce computation time (from over 5 h to less than a second for a single prediction) while keeping the same order of accuracy of CFD simulations for local microclimate prediction of individual buildings. It therefore facilitates the fast, comprehensive and accurate prediction of the impacts on the local microclimate at the early design stage of new construction and renovation of individual buildings, for designers to deliver preferred local microclimate and/or avoid unacceptable microclimate changes.

TOOLS FOR MODELING AND DESIGN IN THE PROCESS OF TRAINING SPECIALISTS IN VOCATIONAL AND TECHNOLOGICAL EDUCATION INSTITUTIONS

The relevance of the research lies in the growing need of future teachers for effective and productive tools for teaching design, which can significantly reduce the time of learning new progressive software tools. With the development of technology and increasing competition in the market, speed and quality of design are becoming critical success factors. The purpose of the study is to study the effectiveness and productivity of using SolidWorks in the process of teaching design, as well as to determine its advantages over other CAD programs. The article analyzes the use of SolidWorks in the modern engineering and pedagogical process, highlights its advantages compared to other CAD programs, and emphasizes its importance for educators in the process of creating and learning new products and technologies. SolidWorks is positioned as an innovative design program that has numerous advantages due to the interface and a wide set of tools that allow you to significantly increase the efficiency and productivity of learning for the work of future educators. The program also provides the ability to perform various analyzes and simulations, which contributes to the improvement of product quality, allowing to identify potential problems in the early stages of design and reduce costs and risks in design. In general, the use of SolidWorks during the educational process provides ample opportunities for training future teachers. This software significantly increases the efficiency and quality of the educational process, making it an integral part of modern pedagogical education. Future innovations and developments in SolidWorks promise to further improve the competitiveness of design at various stages of the educational process.

Operational greenhouse gas emissions of various energy carriers for building heating

The decarbonisation of the building heating sector requires a shift from decentralised fossil fuel heating appliances to systems converting energy carriers with low greenhouse gas (GHG) emissions. However, for certain energy carriers, a considerable portion of GHG emissions arises upstream during production, processing and transportation, rather than during energy conversion. Accurately quantifying these indirect GHG emissions typically requires life cycle assessments, which are often resource-intensive and impractical during the early stages of energy system design. This study introduces operational GHG emissions as a pragmatic metric for the preliminary assessment of energy carrier environmental impact in building heating applications. These operational GHG emissions include both direct CO2 emissions and indirect CO2, CH4 and N2O emissions. Based on a comprehensive literature analysis, average estimates are proposed for the operational GHG emissions of various energy carriers within a European context, including natural gas, oil, coal and wood, as well as the average European and Belgian electricity grid, and hydrogen from various production methods. The findings underscore the significant contribution of indirect GHG emissions, as the selection of the energy carrier with the lowest environmental impact hinges on whether direct emissions alone or the broader operational GHG emissions are considered. By integrating operational GHG emissions into the early design stages of energy systems, stakeholders can make more informed decisions about which energy systems warrant further investigation, thereby facilitating more sustainable energy system development from the outset.

A Hybrid Metaheuristic based Feature Selection Framework for In-silico Mutagenicity Prediction

Mutagenicity is both a toxic risk to humans and an indicator of carcinogenicity. Hence, estimating mutagenicity in the early stages of drug design is crucial to minimize last-stage failures and withdrawals in drug discovery. Recently, in-silico methods have started to play critical and essential roles in the drug development process because they are low cost and low effort procedures. This study aims to predict mutagenicity of chemicals using in-silico methods. To achieve this goal, a two-phased flexible framework was proposed: 1) searching the effective and representative descriptors subset with Butterfly Optimization Algorithm (BOA) and Particle Swarm Optimization and 2) predicting mutagenicity of chemicals by the selected descriptor using gradient boosted tree-based ensemble methods. The study used two datasets: one including 8167 compounds for descriptor selection and modelling, and another containing 716 external compounds to validate the efficacy of our models. The datasets comprise 162 descriptors calculated using PaDEL. The results of both the cross-validation and the external data showed that descriptors reduced by nearly one-third by BOA (51 descriptors) yielded similar or slightly better predictive results than results obtained with the entire data set. The accuracy range attained by the proposed approach using BOA is approximately 91.9% to 97.91% for the external set and 83.35% to 86.47% for the test set. This research contributes that using optimization techniques for improving early drug design and minimizing risks in drug discovery can be considered as a valuable insights and advances in the field of drug toxicity prediction, based on the findings.

Design of a Human Muscle-Powered Flying Machine

This study explores the design and development of a human-powered aircraft (HPA), leveraging modern engineering techniques, materials science, and advanced CAD/CAM tools. The project addresses key aspects of aircraft design, including the geometry of wings and tail, control and power transmission mechanisms, propeller selection, and material identification to achieve ultra-lightweight construction. The 3DExperience platform facilitated comprehensive model creation, simulation, and production process development, while XFLR5 was employed for aerodynamic profile analysis using the vortex lattice and panel methods. JavaProp aided in evaluating propeller thrust and power requirements. Computational fluid dynamics (CFD) simulations using the SST k-ω turbulence model provided critical insights into flow behavior. The design was found to be theoretically capable of flight, although challenges arose in selecting appropriate software for aerodynamic analysis, leading to the use of XFLR5 for early-stage design and the more advanced 3DExperience platform for final evaluations. Although structural strength analyses were not performed due to the complexity of composite materials, future work in this area could enhance the precision of component selection and aircraft mass estimation.

Developing a novel anticipated inherent risk assessment approach applicable in early stages of process design

Inherently Safer Design (ISD), as one of the most fundamental approaches to process safety, focuses on the innate design features. Numerous tools have been developed to assess ISD and risk-based indices are one of the most elevated of them. Existing indices are limited in modelling consequences or are challenging to adapt in terms of probability. This study introduces the Anticipated Inherent Risk Index (AIRI), a robust tool for rapid risk assessment of the worst anticipated scenarios. It incorporates Furthest Effect Distance of the release from the largest connection, by software, and Representative Failure frequency, which is a relative dataset calculated from renown databases. This presents a relative risk map upon major consequences and their likelihood. AIRI applied to a PDH Fractionation Subunits shows that Heat Pump Compressor Suction Drum (AIRI≈3399) in PP Splitter Section, followed by Propylene Trim Condenser (AIRI≈1931) and PP Splitter Reboiler/Condenser (AIRI≈1798) are the equipment with the highest risk. AIRI is a competent tool for early high-level analysis of equipment risk, which gives useful indications on design and how to improve it. This tool also has the potential to be equipped with vaster frequency datasets and effects analysis and to be incorporated into other process safety studies.

Exploring the Manifestation of Design for Manufacturing Heuristics in Students' Early-Stage Engineering Design Concepts

Abstract Additive manufacturing (AM) can produce designs in a manner that greatly differs from the methods used in the older, more familiar technologies of traditional manufacturing (TM). Despite this method of manufacturing designs that were seemingly inconceivable in the past, designers often forgo the new design considerations of AM and instead rely on their familiarity with the design considerations more commonly associated with TM, regardless of the manufacturing process they intend to use. This can lead to unnecessary material usage, increased manufacturing time, and can result in designs which are poorly manufactured. To remedy this problem, there is a need to intervene early in the design process to help address any concerns regarding the use of AM design heuristics. This work aims to address this opportunity through a preliminary exploration of the design heuristics that students naturally leverage when creating designs in the context of TM and AM. With the common design heuristics that students are most likely to use in their designs identified, future studies will cement early-stage design support tools that emphasize the significant heuristics found herein. For example, this work found that the design heuristic “incorporating complexity” was the most significant indicator of designs most suited for AM and should therefore be highly encouraged/emphasized when guiding designers in the use of AM. In doing so, it will be possible for early-stage design support tools to maximally improve designs that are intended to be manufactured for AM.