Conceptual Design Phase Research Articles

Balanced-risk analysis in the engineering design of complex systems with extreme conditions

The engineering design of components and systems in a fusion power plant, an example of a complex system with extreme conditions, requires careful consideration and balance of the numerous associated risks. Key risks are observed from several perspectives and categorised as failure modes, situational and quality risks. Several methods exist for the separate consideration of these risks at the various design phases. However, an optimum approach is to consider all risks and balance the effects during the early design phase. The paper introduces a novel methodology to consider all associated risks at the conceptual design phase by combining a generalised Π-theorem and dimensional analysis for qualitative physics reasoning. The method is applied to a Balance of Plant components as a case study. Initial results show positive concurrent consideration and analysis of all risks in the conceptual stage.

Spatial Big Data and Analysis Strategies Supporting Geographic Information System for Transportation (GIS-T) in Conceptual Design, Modelling, and Decision-making: A Review

Abstract. Transportation is a key component in urban design for cities’ efficiency and residents’ life quality, and GIS has the capability of handling data management, model design, and scientific decision-making for transportation studies. This article reviews how spatial big data and analysis strategies help GIS-T studies from the phases of conceptual design, modelling, and decision-making. In this research, we firstly summarized categorizes of data objects and relevant information for real-world issues from transportation applications in the conceptual design phase. In the modelling phase, optimization strategies for transport planning and accessibility measures through network data were also summarized. Finally, we reviewed spatial analysis methods in supporting transport decision-making, and how spatial methods take advantage of geography features of transport variables in previous studies. Our research primarily focuses on geography research with transportation topics as applications, and this work can help transport experts to have a better understanding of GIS values for transportation modelling and planning.

Multi-objective ergonomics design model optimization for micro electric cars via response surface methodology

Despite advancements in ergonomic comfort assessments in automotive design, optimizing seat dimensions within the constrained spaces of micro-electric cars presents a substantial challenge. In this study, response surface methodology (RSM) is utilized for the ergonomics design of a micro electric car in the conceptual design phase. Specifically, five critical seat dimensions are analyzed: Seatback Angle, Seat Base Angle, Steering Wheel Height from Car Base, Distance from Seat Base to Pedals, and Distance from Seat Base to Steering Wheel. The analysis took place using a 2-level full factorial design L32 orthogonal array. The optimal dimensions are determined using RSM, such as the mean comfort level and signal-to-noise ratio are maximized, and the standard deviation for multiple drivers is minimized. Three regression models are formulated for the three responses, and their adequacy is checked using different methods. The optimal dimensions are obtained and verified through digital human modeling simulation. This research offers practical guidelines for the ergonomic seating design in micro-electric vehicles, enhancing comfort without compromising space efficiency.

Design and Optimization of LPG Safety Cap using Finite Element Method

This paper presents a comprehensive study on the design and optimization of LPG safety caps, aimed at enhancing safety, reducing material consumption, and optimizing performance. The project employs SolidWorks simulation tools to conduct research and concept design phases. The primary objective is to ensure product safety while minimizing costs through Finite Element Analysis (FEA). The final product undergoes rigorous evaluation for performance and consumer safety. Three main goals guide this investigation: to scrutinize existing LPG safety cap designs, minimize material usage, and simulate the optimal design for enhanced safety. Analysis of the current design reveals vulnerabilities, including theft of LPG gas from sealed cylinders and escalating HDPE prices. Proposed designs simplify the current model and economize material consumption, successfully addressing the second objective. Through simulation, an optimal design (Design 8) emerges with superior characteristics compared to the existing model. Design 8 demonstrates a reduced total weight, higher safety factor, and lower maximum von Mises stress value. Notably, it exhibits enhanced safety and resilience, mitigating failure risks under stress conditions. The findings highlight the potential for creating stronger and safer designs while minimizing material requirements. In conclusion, Design 8 emerges as a superior alternative to the current design, boasting advantages in weight reduction, stress tolerance, safety factor, and optimization potential. This study underscores the significance of utilizing advanced computational methods to refine engineering designs for improved safety and efficiency in LPG safety caps.

Refined air-cooled battery sizing process for conceptual design of eVTOL aircraft

Recently, electric vertical takeoff and landing (eVTOL) aircraft have garnered significant interest as a primary mode of transportation in advanced air mobility (AAM). For the conceptual design of eVTOL aircraft, where a broad design space must be explored rapidly, a practical and reliable battery sizing process is essential. This process needs to employ models with low computational cost while comprehensively considering two key factors: voltage drop characteristics and thermal effects. However, existing research on battery sizing mainly relies on constant specific energy or power, neglecting these factors. This oversight can lead to significant discrepancies in battery sizing between the conceptual and detailed design phases. To address these limitations, this paper introduces a refined battery sizing process. Our approach incorporates models for battery voltage drop, heat generation, and a cooling system. A Thevenin equivalent circuit is used to model voltage drop and heat generation, with a novel calibration method developed for accurate predictions of voltage and temperature profiles. An air cooling system is adopted for its simplicity and lightweight design, and a corresponding model is constructed. Applying our process to eVTOL aircraft sizing revealed that incorporating temperature, along with the depth of discharge (DoD) and C-rate, as constraints in battery sizing led to a 4.6 % decrease in specific energy and a 4.2 % decrease in specific power. These findings underscore the importance of considering temperature in battery sizing. Additionally, sensitivity analyses provided insights into which thermal management system, air-cooled or liquid-cooled, is more appropriate for the battery under various operating conditions.

A module layout design tool for off-site factory construction: Reactor Auxiliary balance of plant systems case study

There is significant interest in off-site modular factory construction for nuclear power. The IAEA defines Small Modular Reactors as “factory shop built and transported to site” and lists over 30 water cooled, 14 high temperature, 10 fast neutron, 10 molten salt, and 8 micro reactors in development worldwide. Off-site modular construction is a new development and offers more reliability in the construction and nuclear industries. A tool to help designers navigate the plethora of increased options and design challenges that modular design presents has therefore been identified as possibly increasing the efficiency and effectiveness of the design process, especially in the concept design phase. This study found that the method can create a starting point for design engineers to iterate and improve designs, significantly reduce design time in finding improved solutions, and improve performance and reduce costs associated with pipe length and network flows around the plant.

Predicting maximum deflection of N-Edged thin-shelled hyperbolic-Paraboloid umbrella using machine learning techniques

Many thin-shelled hyperbolic paraboloid (hypar) umbrella forms have been built in the last 60 years as roof coverings. While the stresses in these forms remain relatively low, the deflections are a critical design parameter, and one that must be considered by architects and engineers in the conceptual design phase, meaning the design stage when the scale and form is given to the structure. To-date, there is no closed-form solution that can predict the maximum deflection of umbrellas due to their highly varying and complex geometry. The best option for predicting deflection is via finite element analysis, which is time-consuming for conceptual (preliminary) design purposes. In response, this paper uses machine learning via genetic programming (GP) and gene expression programming (GEP) to develop closed-form equations that predict the maximum corner deflection of N-edged hypar umbrellas – where N = 3, 4, 5, 6, 7, and 8. For a given a boundary condition and material, geometry is the most significant parameter influencing a shell's stiffness; thus, elastic finite element (FE) models use geometric properties as input variables (N, projected area, normalized rise, and shell thickness). The maximum corner deflection is recorded as an output and the FE analyses generate a large dataset of 53,754 results. It is observed that both GP and GEP can effectively parameterize the maximum deflection of N-edged hypar umbrellas, with GEP producing more concise, but relatively less accurate, equations than GP. While the formulations are trained using concrete material, a material factor multiplier transforms the results to other material properties within the assumption of elastic limits. The results of the study can be used to assist with conceptual design of hypar umbrellas and to validate complex FE models of hypar umbrellas. This research also illustrates the use of machine learning techniques as applied to the conceptual design of structures with highly varying geometries.

Conceptual design and analysis of a box fan-in-split-wing tiltrotor eVTOL aircraft

PurposeWith the advancements in electric vertical take-off and landing (eVTOL) aircraft technology such as batteries, mechanisms, motors, configurations and so on, designers and engineers are encouraged to create unique and unconventional configurations of eVTOL aircraft to provide better capabilities and higher efficiencies to compete in the market. The box fan-in-split-wing tiltrotor eVTOL aircraft is an innovative design that aims to address the aerodynamic inefficiencies such as propeller effects in cruise and engine mounts drag that existed in traditional eVTOL aircraft designs such as vectored thrust, rotorcraft, lift + cruise and multi-copter configurations. This paper aims to propose a multi-disciplinary design process to conceptually design the box fan-in-split-wing Tiltrotor eVTOL aircraft.Design/methodology/approachAn unconventional methodology was used to design the UAM aircraft, and the following parameters are considered: capable of vertical take-off and landing, highly aerodynamic with a high lift-to-drag ratio, low Cd0 modern and appealing, rechargeable or battery swappable and feature to minimise or negate propeller drag. A heavy emphasis on improving performance and weight based on aerodynamics was enforced during the conceptual design phase. MAPLA and XFOIL were used to identify the aerodynamic properties of the aircraft.FindingsUpon determining the key parameters and the mission requirements and objectives, a list of possible VTOL configurations was derived from theoretical and existing designs. The fan in the wing/split wing was selected, as it could stow the propellers. A tiltrotor configuration was selected because of its ability to reduce the total number of lift props/motors, reducing powerplant weight and improving aerodynamic efficiency. For the propulsion configuration, a battery–motor configuration with a hexa-rotor layout was chosen because of its ability to complement the planform of the aircraft, providing redundant motors in case of failure and because of its reliability, efficiency and lack of emissions. Coupled with the fan-in-wing / split wing concept, the box wing seamlessly combines all chosen configurations.Originality/valueThe box fan-in-split-wing Tiltrotor eVTOL aircraft aims to address the aerodynamic inefficiencies of earlier designs such as propeller effects in cruise and engine mounts drag. The potential benefits of this aircraft, such as increased range, endurance and payload capacity, make it an exciting prospect in the field of Urban Air Mobility.

Designing 3D-printed concrete structures with scaled fabrication models

This article proposes using scaled fabrication models to assist the design research of 3D-printed discrete concrete structures where full-scale fabrication tests are costly and time-consuming. A scaled fabrication model (SFM) is a scaled model 3D-printed the same way as in actual construction to reflect its fabrication details and acquire alike layer line textures. The components of a 1:10 SFM can be easily produced by consumer-level desktop 3D printers with minimal modification. SFMs assist the design communication and make possible quick tests of distinct fabrication designs that are hard to assess in digital modeling during the conceptual design phase. A case study of a discrete compression-dominant funicular floor derived from graphic statics is presented to illustrate the contribution of SFM to the design research of force-informed toolpathing where the printing direction of a component is aligned to the principal stress line. The design iterations encompass a sequence of component, partial, and full model SFM printing tests to explore and optimize the fabrication schemes where parallel, non-parallel, and creased slicing methods to create toolpaths are compared and chosen to adapt different discrete components.

Onboard carbon capture and storage (OCCS) for fossil fuel-based shipping: A sustainability assessment

Limiting the carbon intensity of maritime transport is crucial to meet 2050 net-zero targets. Onboard carbon capture and storage (OCCS) offers a practical short-term solution for reducing shipping-related CO2 emissions until cleaner technologies are ready for large-scale adoption. This study introduces an innovative multi-objective approach to integrate sustainability into the conceptual design and decision-making phases of OCCS. A systematic technology screening identified possible OCCS solutions, which were then assessed for onboard feasibility. Specific indicators were defined to evaluate OCCS performance based on technological, economic, environmental, and social criteria, and aggregated sustainability perspectives. Using a fossil fuel-powered cruise ship as a case study, results were benchmarked against zero-carbon alternatives. Among the alternatives considered, chemical absorption by amine scrubbing (AS) and advanced cryogenic carbon capture (A3C) appeared as the only feasible solutions considering onboard energy requirements. The emerging cryogenic A3C concept resulted in being outperformed by benchmark AS, primarily due to an environmental impact 1.5 times higher. All alternative technologies, whether OCCS- or cleaner fuel-based, were found to be more sustainable than the baseline fossil fuel-based engine, lowering the environmental impact by at least 61%. Hydrogen as a marine fuel leads to the most promising scenario for future cleaner shipping operations, reducing the sustainability footprint by up to 76%. The robustness of the proposed method was confirmed by a probabilistic Monte Carlo sensitivity analysis. Overall, the results obtained can guide toward more informed solutions and policies promoting the sustainability of ship propulsion systems.

Integrated Waverider Forebody/Inlet Fusion Method Based on Discrete Point Cloud Reconstruction

The integrated design of waverider forebodies and inlets is considered a critical challenge in high Mach number vehicle development. To facilitate the rapid construction of integrated geometrical models for waverider forebodies and inlets during the conceptual design phase, a method based on discrete point cloud reconstruction has been proposed. In this method, the geometries of the waverider body and inlet are used as inputs and decomposed into the point cloud under discrete rules. This point cloud is refitted to generate new section lines, which are then lofted into an integrated shape under the constraints of guide curves. By modifying the coordinates of the point cloud positions, the geometric configuration of the integrated shape can be rapidly adjusted, providing initial support for subsequent aerodynamic optimization and thermal protection. Using this method, an integrated approach was applied to a waverider forebody and inward-turning inlet in a tandem configuration. This achieved body-inlet matching and integration, resulting in a 15.6% improvement in the inlet’s total pressure recovery coefficient. The integration time was reduced to just 3.18% of the time required for traditional manual adjustments. Additionally, optimization based on the discrete point cloud enhanced the lift-to-drag ratio by 7.83%, demonstrating the feasibility of the proposed method.

Optimal design and fabrication of frame structure for dual-arm service robots: An effective approach for human–robot interaction

Rapid advancement in robotics technology has paved the way for developing mobile service robots capable of human interaction and assistance. In this paper, we propose a comprehensive approach to design, fabricate, and optimize the overall structure of a dual-arm service robot. The conceptual design phase focuses on both critical components, the mobile platform and the manipulation system, essential for seamless navigation and effective task execution. In the proposed system, the distribution of the robot payload in terms of region, maximum stress, and displacement is examined, comprehensively analyzed, and compared with the relevant works. In addition, to enhance the system’s efficiency while minimizing its weight, we introduce a lightweight design approach in which Finite Element Analysis is utilized to optimize the frame structure. Subsequently, we fabricate a physical prototype based on the derived model. Finally, we provide a kinematic model for our dual-arm service robot and demonstrate its efficacy in both control and human–robot interaction (HRI) tasks. Experimental results indicate that the proposed dual arm design can achieve a significant weight reduction of 25% from the original design while still performing actions smoothly for HRI tasks.

Development of Scenarios as Problem-Space Descriptions in Aerospace Conceptual Design

In the defense and security domain, scenarios are often descriptions of stakeholder needs, future events, and the environment. They are used for the elicitation of requirements in development of capabilities, organizations, and technical systems. In the conceptual design of aerospace applications, models of scenarios can also represent and communicate a problem-space, enabling trade-space exploration and system effectiveness robustness analysis, which provide valuable input to decision-makers. This study utilizes design science to develop a scenario framework for solution-agnostic representations of a problem-space for use in aerospace conceptual design- and trade-space exploration. A scenario ontology is developed, describing the constituent concepts of scenarios and their relationships, followed by a method for creating scenarios and evaluating their validity. Within the EU project COLOSSUS, it is demonstrated that the scenario framework has utility both for market-pull and technology-push conceptual design. Establishing an ontology for scenarios and a method for creating them as well as evaluating their validity is another step in improving the aerospace conceptual design phase.

Design of an Extrudate Filament Machine for Recycling Waste Polyethylene Terephthalate Plastics into 3D Printing Filament

This research paper presents the design of an extrudate filament machine tailored for recycling waste Polyethylene Terephthalate (PET) plastics into filament suitable for 3D printing. The exponential growth of plastic waste, particularly PET, poses significant environmental challenges. Recycling PET into usable filament for 3D printers not only addresses environmental concerns but also promotes sustainable manufacturing practices. The design process encompasses a thorough analysis of requirements, considering factors such as input material variability, filament diameter consistency, and production throughput. Through conceptual and detailed design phases, the machine is optimized to effectively shred, melt, extrude, cool, and wind the recycled PET material into uniform filament spools. Material selection is carefully curated to ensure durability, thermal stability, and compatibility with PET recycling processes. The machine integrates various components such as shredders, extruders, cooling systems, and filament winding mechanisms to efficiently process waste PET plastics. Furthermore, the paper explores the economic feasibility and environmental benefits of implementing such a system for local plastic recycling initiatives.

Conceptual Design of Offshore Jacket Substructures Using Machine Learning

The offshore wind sector is mature and has led to standardized design methods for offshore substructures. The conceptual design phase is critical for efficiency and cost-effectiveness and forms the basis for further design iterations. As turbine capacity increases, so does the complexity of offshore substructures, making design more challenging. However, the design process still relies on the expertise of the design engineers. These engineers rely heavily on their experience and intuition when designing, which can lead to biases due to limited information. To address this problem, Machine Learning (ML) techniques offer a promising way to improve the accuracy and efficiency of the conceptual design of offshore substructures. The current study is limited to the conceptual design of jacket substructures and was conducted on a self-developed global dataset of real jackets. The ML-based approach proposed in this study is capable of learning from existing data, recognizing intricate relationships between design variables, and potentially providing more accurate estimates for the initial conceptual design of offshore jacket substructures.

Interval-valued spherical fuzzy quality function deployment methodology: Metaverse collaborative system design application

The metaverse collaborative system (MCS) aims to assist users in working together, sharing resources, and participating in joint activities in the metaverse environment. As an interactive system, identifying essential design requirements (DRs) based on user requirements (URs) during the conceptual design phase of MCS is challenging as it directly affects the cost of subsequent optimization design and user experimentation, as well as user experience. Therefore, we propose a quality function deployment (QFD) method based on multi-criteria decision-making (MCDM) in an interval-valued spherical fuzzy (IVSF) environment to establish a mapping relationship between URs and DRs and identify human-centered DRs to guide the conceptual design process of MCS. Firstly, we summarized the URs for MCS design and calculated their weights using IVSF based analytical hierarchy process (IVSF-AHP). Afterward, we determined DRs based on URs and established the UR-DR relationship in the house of quality of QFD. Next, we integrate the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method in the IVSF environment into QFD to determine the priority of DRs. The results indicate that system functional design, scene layout design, compatibility design, and multi-user collaborative optimization design are important DRs for designing MCS and should be carefully considered. We validated the applicability of this method through an urban planning MCS design example and provided a conceptual design case including functional blueprints. Sensitivity analysis and comparative analysis demonstrate the flexibility and reliability of the proposed method.

Chinese Immigrant Caregivers: Understanding Their Unmet Needs and the Co-Design of an mHealth App.

Immigrant caregivers support the aging population, yet their own needs are often neglected. Mobile technology-facilitated interventions can promote caregiver health by providing easy access to self-care materials. This study employed a design thinking framework to examine Chinese immigrant caregivers' (CICs) unmet self-care needs and co-design an app for promoting self-care with CICs. Nineteen semi-structured interviews were conducted in conceptual design and prototype co-design phases. Participants reported unmet self-care needs influenced by psychological and social barriers, immigrant status, and caregiving tasks. They expressed the need to learn to keep healthy boundaries with the care recipient and respond to emergencies. Gaining knowledge was the main benefit that drew CICs' interest in using the self-care app. However, potential barriers to use included issues of curriculum design, technology anxiety, limited free time, and caregiving burdens. The co-design process appears to be beneficial in having participants voice both barriers and preferences.

Incorporating failure mode and effects analysis into a novel framework for hydrogen production from solid waste gasification

Gasifying waste represents a sustainable and highly effective approach; however, ensuring rigorous control of hydrogen leakage is paramount to guarantee both safety and long-term sustainability. Due to the distinctive features of these production facilities and the likelihood of their placement in public municipal spaces, it is imperative to prioritize a comprehensive approach to ensuring the safety of these installations. This includes a thorough evaluation of the risks linked to hydrogen and taking proactive measures to prevent accidents, a crucial aspect that has been neglected in prior research endeavors. Therefore, this article introduces an enhanced iteration of the risk management framework based on the failure mode and effects analysis (FMEA) and the evaluation based on distance from average solution (EDAS) methods, implemented in a municipal solid waste gasification system to facilitate hydrogen production. The research employs FMEA to scrutinize potential failure modes, strategically prioritizing them through advancements in the EDAS methodology. Notably, this framework is seamlessly integrated during the conceptual design phase, showcasing its efficacy in bolstering risk-informed design strategies. Through rigorous comparative and sensitivity analyses, the resilience of the framework is underscored. The refined FMEA-EDAS framework holds substantial practical implications for managing hydrogen production facilities by providing a structured approach to resource allocation and ensuring adherence to industry standards. Furthermore, it fosters interdisciplinary collaboration, potentially serving as a knowledge hub to fortify safety measures within the realm of hydrogen production.

New combination of methods for supporting a simplified set-based design approach

AbstractThis work presents a Set-Based Design inspired approach for generation, evaluation and reduction of a solution space of alternative product concepts. Also, the aspect of how to implement the process in an industrial environment was investigated. The hypothesis, confirmed by case studies, is that it can be done using existing methods like Enhanced functional modelling, brainwriting, the Gallery method, Axiomatic Design, causal diagrams and Pugh matrices. The method can be successfully introduced in a timeframe of a few working days and support development engineers in the concept design phase.

Fostering innovation through bio-inspired projects in engineering design education

AbstractThis paper discusses the C-K theory approach and developing templates for student's use in design courses, specifically for the conceptual design phase. Examples of C-K templates are reviewed to demonstrate the process leading to a design solution. Analysis of student's work using these templates are presented and discussed with respect to design learning attributes and design innovation during the conceptual design. Student reflections from their final design reports indicated that students did develop knowledge and skills in bio-inspired design, collaboration, and interdisciplinary mindsets.