Early Design Stage Research Articles

Leveraging the concept of information entropy to improve a multi-fidelity design framework for early-stage design exploration of complex vessels

Early-stage design exploration is crucial since most of the major design decision are locked-in and only small design modifications are possible at later stages. To assess the performance of the various design candidates while performing design exploration, there are available methods and tools of various fidelities. These methods can be combined to form a multi-fidelity (MF) framework that guarantees accuracy through the high-fidelity model and achieves faster computational speeds through low-fidelity models. The present study proposes the adoption of information-theoretic entropy to improve a MF design framework based on Gaussian Processes (GPs). Entropy quantifies the uncertainty associated with the prediction of the design space. We propose using this uncertainty metric both as a criterion to determine whether further designs should be sampled to construct a reliable approximation of the design space and as a criterion to establish in which optimization step the optimization of the covariance matrix for the MF-GPs should be performed. The approach was tested to benchmark analytical functions and to a ship design problem of an AXEfrigate. The approach holds potential in practical applications, as it aids in the determination of whether additional resources should be allocated for high-fidelity analysis to support early-stage exploration.

Paper or Tablet? The Impact of Digital Tools on Sketching During Engineering Design Concept Generation

Abstract Sketching is an important tool for engineers during concept generation. Sketch quantity during this early stage of design has been linked with eventual design outcomes and sketch quality has been linked with design perceptions. As such, both are important metrics to track. Prior work has also found gender differences in some of these sketch attributes, and considering gender imbalances in the field, it is important to assess whether there are differences in performance by gender during concept generation and if the use of digital tools exacerbate or mitigate any of these potential differences in performance by gender. Given the increase in use of digital tablets for sketching, it is important to assess how tablet use affects early stage engineering design sketches. This is especially important as tablets can automatically smooth lines and help perfect sketch features, but these features may also take longer to use and may result in fewer sketches produced. This study investigates differences in sketch quality, quantity, and understandability (the effectiveness of the sketch as a communication tool) between sketching on a tablet and sketching with pen on paper during an engineering design concept generation exercise. Results indicate that there is no difference in sketch quantity or understandability between the two tools. However, sketch quality, smoothness, and proportion/accuracy are all higher for the pen and paper condition than for the tablet condition. Finally, no gender differences in performance for either sketch quantity or quality were found.

Grounded Ambitions: A Lean Approach for Assessing Beachability in Concept Design

Littoral operations have become an increasing interest for defense stakeholders over the last several decades. Many navies currently operate ship-to-shore assets that are designed to travel shorter distances exclusively in the littorals between a ship and the beach. New concepts are being designed to transit much longer distances from shore-to-shore in both blue water and littoral regions. This Concept of Employment (CONEMP) drives these ships to displacements that are orders of magnitude larger. Compared to smaller vessels where seakeeping and maneuverability performance in the surf-zone are a significant area of interest, larger vessels have a comparatively greater risk with respect to the ability of the ship to get far enough up a beach to safely deliver assets and then get off the beach. This research presents the foundation for a new simulation tool to analyze how far up the beach a ship will be able to get given loading condition, initial speed, beach condition, and hull shape. The focus of this research is to provide a low computational-cost method for analyzing the beachability of a ship that still considers the dominating physical phenomena of grounding at early stages of design. The tool will need much faster turnaround times than high-fidelity Reynolds-Averaged Navier-Stokes (RANS) or Finite Element Analysis (FEA) simulations to support the rapid and evolving environment of concept design timelines.

Improving Ship Response Estimation using Neural Networks

The feasibility of a data-adaptive multi-fidelity seakeeping model is assessed for use in early stage design in this study. Data adaptive tuning (or correction) of lower-fidelity model predictions are implemented based on training with higher fidelity ship motion response data. Long Short-Term Memory (LSTM) neural networks are incorporated as part of a multi-fidelity approach for prediction of 6 degree of freedom (6-DOF) ship motion responses in waves. LSTM networks are trained and tested with Large Amplitude Motion Program (LAMP)simulations as a target, and SimpleCode simulations and wave time series as inputs. LSTM networks improve the fidelity of SimpleCode seakeeping predictions relative to LAMP, while retaining the computational efficiency of a lower-fidelity simulation tool.

Prediction of shear wall residential beam height based on machine learning

The beam height is an important design parameter that influences structural properties such as load-bearing capacity and stability of beams. In the early stages of structural design, the existing methods for determining beam height mainly include empirical formulae. However, empirical methods are highly subjective, lack accuracy, and are poorly adapted to complex conditions. This paper establishes a beam height prediction model for shear wall residential structures. Using structural design data from projects built by a real estate company across various regions in China, a large dataset of beam heights was collected. The Permutation Feature Importance (PFI) method and six unique machine learning (ML) models were used to rank the importance of input variables. The Gradient Boosting (GB) model, consistent with the feature ranking obtained from PFI, was selected. The model evaluation method was then used to select the number of input features for the GB model, and grid search and K-fold cross-validation were employed to optimize the GB prediction model. This model was compared with a prediction model obtained from a Back Propagation Neural Network (BPNN). Finally, the SHAP method was used to interpret the "black box" machine learning model. The results show that the GB model has higher accuracy compared to the BPNN model, and the input features of the proposed GB model contribute to the beam height in accordance with mechanical laws, demonstrating the model's rationality. The research findings can provide a reference for initial beam height design.

Towards Controllable Generative Design: A Conceptual Design Generation Approach Leveraging the FBS Ontology and Large Language Models

Abstract Recent research in the field of design engineering is primarily focusing on using AI technologies such as Large Language Models (LLMs) to assist early-stage design. The engineer or designer can use LLMs to explore, validate and compare thousands of generated conceptual stimuli and make final choices. This was seen as a significant stride in advancing the status of the generative approach in computer-aided design. However, it is often difficult to instruct LLMs to obtain novel conceptual solutions and requirement-compliant in real design tasks, due to the lack of transparency and insufficient controllability of LLMs. This study presents an approach to leverage LLMs to infer Function-Behavior-Structure (FBS) ontology for high-quality design concepts. Prompting design based on the FBS model decomposes the design task into three sub-tasks including functional, behavioral, and structural reasoning. In each sub-task, prompting templates and specification signifiers are specified to guide the LLMs to generate concepts. User can determine the selected concepts by judging and evaluating the generated function-structure pairs. A comparative experiment has been conducted to evaluate the concept generation approach. According to the concept evaluation results, our approach achieves the highest scores in concept evaluation, and the generated concepts are more novel, useful, functional, and low-cost compared to the baseline.

Characterizing three-dimensional general arrangements and distributed system configurations utilizing an architecturally normalized current representation

Designing ships involves intricate layouts and multifaceted systems—ranging from mechanical to operational—that must be interdependent and thus precisely arranged. Traditional automated tools, though effective, are often too resource-intensive to be feasibly employed during the critical early stages of design. This paper builds on prior work that introduced an innovative solution: a network-based, architecturally normalized current representation, which offers a computational method to predict system arrangements in two dimensions without generating detailed vessel models. Our method’s advantage lies in its ability to guide early stage design decisions, thereby optimizing the use of subsequent, more resource-intensive design tools. This study extends the method to a three-dimensional framework, capturing more nuanced system-to-system interactions and yielding more realistic ship arrangements. A methodology was proposed to support this three-dimensional extension and demonstrate its applicability through a case study focused on the conceptual design of a naval frigate.

Heat Transfer Models and Measurements of Brushless DC Motors for Small UASs

Heat transfer affects a motor’s sizing, its performance, and, ultimately, the overall vehicle’s range and endurance. However, the thermal literature does not have early-stage models for outrunner brushless DC (BLDC) motors found in small unmanned aerial systems (UASs). To address this gap, we have developed a non-dimensional heat transfer model (Nusselt correlation). Parametric experiments of four different-sized BLDC motors under load in Reynolds-matched wind tunnel tests generated data for model correlation. The motors’ aspect ratios (diameter/length) ranged from 0.9 to 1.5. The freestream Reynolds number of the axial flow over the motors ranged from 20,000 to 40,000. The rotational Reynolds number ranged from 10,000 to 20,000. The results showed that aspect ratio had the largest influence on heat transfer, followed by rotational and freestream Reynolds numbers. A steady-state model used the correlation to predict the motor’s ambient temperature differential within 10 K of experimental data. A case study applied the correlation to predict a hypothetical motor’s continuous torque in different environments. The correlation enables conceptual designers to capture thermally-driven trade-offs in early design stages and reduce costly revisions in later stages.

Plantwide flexibility analysis of a biogas to methanol process: Assessing the implications of uncertainties on process duties and emissions

During the last decade, the issue of major concern for the process industry is the reconversion of conventional chemical processes into more sustainable ones, especially in the light of the emission goals set by the EU for 2050. For this purpose several technologies exploiting cogeneration, biomasses and energy integration have been recently conceived. However, these strategies usually imply a reduction in terms of degrees of freedom and a higher sensitivity of the process to eventual uncertainties. In this case, external perturbations could compromise the profitability and sustainability of the system and the design based on nominal operating conditions is not sufficient anymore to assess the actual performance of the plant. In this research work the challenge concerning negative emissions plants and their performances under perturbed conditions is discussed in detail in order to outline and provide a quantitative methodology to enable a more conscious decision since the early design stage.

Mechanised deep tunnel excavation in saturated clayey soils: a pre-design hydro-mechanically coupled method for the assessment of both spoil and face volume loss

Mechanised tunnel excavation in soils causes over-excavations, potentially leading to large amounts of spoil and settlements at ground level. An accurate estimation of over-excavations is crucial in the pre-design phase for assessing costs, determining the appropriate excavation method and choosing the muck management strategy. Currently, the estimation is based on experience and data from similar projects, but this becomes difficult when project conditions are heterogeneous. As an alternative, finite-element analyses are time-consuming and not suitable for early design stages; therefore, simplified tools are needed. In this paper, the authors present a simplified approach putting in relation face extrusion with estimated spoil mass and face volume loss. This approach, conceived for deep tunnels, is the extension to the case of mechanised tunnelling of a hydro-mechanical coupled meta-model derived from finite-element numerical analyses for tunnels in clayey soils excavated by using conventional techniques (i.e. without any use of tunnel-boring machines). The model has been validated against field data relative to a case study. The approach can be used in the early design process to identify tunnel-boring machine characteristics and provide preliminary cost estimates. In addition, during the construction phase, the method can be employed to interpret monitoring data and pre-design mitigation measures for unforeseen soil profile variations.

Early-life factors shaping the gut microbiota of Common buzzard nestlings

BackgroundExploring the dynamics of gut microbiome colonisation during early-life stages is important for understanding the potential impact of microbes on host development and fitness. Evidence from model organisms suggests a crucial early-life phase when shifts in gut microbiota can lead to immune dysregulation and reduced host condition. However, our understanding of gut microbiota colonisation in long-lived vertebrates, especially during early development, remains limited. We therefore used a wild population of common buzzard nestlings (Buteo buteo) to investigate connections between the early-life gut microbiota colonisation, environmental and host factors.ResultsWe targeted both bacterial and eukaryotic microbiota using the 16S and 28S rRNA genes. We sampled the individuals during early developmental stages in a longitudinal design. Our data revealed that age significantly affected microbial diversity and composition. Nest environment was a notable predictor of microbiota composition, with particularly eukaryotic communities differing between habitats occupied by the hosts. Nestling condition and infection with the blood parasite Leucocytozoon predicted microbial community composition.ConclusionOur findings emphasise the importance of studying microbiome dynamics to capture changes occurring during ontogeny. They highlight the role of microbial communities in reflecting host health and the importance of the nest environment for the developing nestling microbiome. Overall, this study contributes to understanding the complex interplay between microbial communities, host factors, and environmental variables, and sheds light on the ecological processes governing gut microbial colonisation during early-life stages.

Enhancing Furniture Manufacturing with 3D Scanning

Product design and manufacturing leverage 3D scanning for various applications. This study aims to investigate the effectiveness of 3D scanning in furniture production by surveying the literature and showcasing four real-world case studies. The literature review reveals that 3D data acquired from real-world objects have applications in research, rapid prototyping, restoration, and preservation of antique furniture, optimizing CNC machining processes, and measuring furniture components for quality control. The case study descriptions demonstrated the circumstances, rationale, and methodology for 3D scanning. All the case studies analyzed stem from the collaboration between the Laboratory for Product Development and Design at the Faculty of Mechanical Engineering at the University of Sarajevo and various furniture production enterprises from Bosnia and Herzegovina. The conclusions highlight that 3D scanning in the furniture sector is advantageous for developing computer-aided design models from early-stage design prototypes, validating the dimensional accuracy of manufactured components by comparing with CAD models, safeguarding and reconstructing vintage furniture, and remanufacturing formerly produced goods that lack complete technical records (reverse engineering).

Research on the Energy Consumption Influence Mechanism and Prediction for the Early Design Stage of University Public Teaching Buildings in Beijing

The public teaching buildings of universities have a large flow of people, high lighting requirements, and large energy consumption, which present significant potential for energy saving. The greatest opportunity for integrating “green” architectural design strategies lies in the design phase, especially the early stage of architectural design. However, current designers often rely on experience or qualitative judgment for decision-making. Thus, there is a pressing need for rational and quantitative green architectural design theories and techniques to guide and support decision-making for the design parameters of teaching buildings. This study, based on field surveys of 40 teaching buildings, constructs building archetypes regarding energy consumption including 28 typical values. Based on the “Rectangle”, “L”, “U”, and “Courtyard” archetypes, through batch energy consumption simulation and multiple regression methods, the influence mechanisms of nine energy consumption influencing factors on four types of building energy consumptions were explored, and energy consumption prediction models were derived. The findings of this research can serve as factor evaluation and selection in the early stage of architectural design for public teaching buildings at universities, and the prediction model can assist in the early estimation of energy consumption. This aims to enrich and supplement green architectural design methods by supporting the design of green public teaching buildings and providing reference and application for relevant engineering practices.

Integrating Pix2Pix and computational fluid dynamics for enhanced indoor airflow prediction: A case study with wing-walls

Computational Fluid Dynamics (CFD) is often used for analyzing natural ventilation but is limited in early design stages due to its high computational demands and the need for detailed inputs. To overcome such limitations of CFD simulation, this paper proposes a method using a conditional Generative Adversarial Network (cGAN) based image-to-image translation (Pix2Pix) to predict the indoor airflow condition. Compared to other traditional machine learning models that predict averaged values like indoor air velocity, Pix2Pix can predict contour plot of indoor airflow for the given building floor plan image, which provides more intuitive feedback to designers. The proposed method was tested to understand indoor air movement caused by wing-walls attached to the windows. The model was trained using 1153 pairs of floor plan images, incorporating variations in window widths, wing-wall depth, wing-wall angle, wind speed, and wind direction. It achieved a prediction accuracy of 94 % and produced results in less than a second. Moreover, the model showed relatively better performance with the changes in windows and wing-walls properties rather than wind properties. This high performance indicates that Pix2Pix can serve as an efficient proxy model of conventional CFD simulations, helping designers optimize ventilation in building designs at an early stage without the need for complex inputs.

The Karakuri IoT toolkit: a collaborative solution for ideating and prototyping IoT opportunities

AbstractThis paper presents a collaborative solution developed to enable people without prior Internet of Things (IoT) knowledge to ideate, conceptualise, role-play and prototype potential improvements to their work processes and environments. The solution, called the Karakuri IoT toolkit and method, was tested in two workshops with eight production leaders at a Swedish manufacturing company. Outcomes were analysed from the perspectives of materials interaction and instruments of inquiry. Results indicate the solution can help people conceive and prototype improvement ideas at early design stages.

Component uncertainty importance measure in complex multi-state system considering epistemic uncertainties

Importance measures can be used to identify the vulnerable components in an aviation system at the early design stage. However, due to lack of knowledge or less available information on the component or system, the epistemic uncertainties may be one of the challenging issues in importance evaluation. In addition, the properties of the aircraft system, which are the fundamentals of the component importance measure, including the hierarchy, dependency, randomness, and uncertainty, should be taken into consideration. To solve these problems, this paper proposes the component Uncertainty Integrated Importance Measure (component UIIM) which considers multiple epistemic uncertainties in the complex multi-state systems. The degradation process for the components is described by a Markov model, and the system reliability model is developed using the Markov hierarchal evidential network. The concept of integrated importance measure is then extended into component UIIM to evaluate the component criticality rather than the component state change criticality, from the perspective of system performance. A case study on displacement compensation hydraulic system is presented to show the effectiveness of the proposed uncertainty importance measure. The results show that the component UIIM can be an effective method for evaluating the component criticality from system performance perspective at the system early design.

A tradespace exploration approach for changeability assessment from a system-of-systems perspective: application from the construction machinery industry

AbstractThe rapid development of new technologies such as electrification, autonomy, and other contextual factors pose significant challenges to development teams in balancing competing aspects while developing value-robust solutions. One approach for achieving value robustness is designing for changeability. This paper presents a tradespace exploration from a Systems-of-Systems perspective to facilitate changeability assessment during early design stages. The approach is further demonstrated on a fleet of haulers operating in a mining site.

Perception-centric design considerations for low-cost haptic emulation in prototypes

AbstractUser-testing is crucial in modern product design. The perception-centric design philosophy aims to cut costs and improve responses to low-cost prototypes by including aspects like thermal properties, texture, weight, sound, and haptic feedback. This paper introduces a set of considerations for integrating low-cost vibrotactile haptics into prototypes. Derived using an action-based research process, it addresses product characterisation, actuation, control, and integration. Multi-sensory prototypes in early-stage design could be vital for the sustainable prototyping of the future.

Design of the cryogenic moderator system for the second target station

The Second Target Station (STS) at Oak Ridge National Laboratory will be a 700 kW pulsed spallation neutron source designed to provide the world’s highest brightness cold neutron beams. In order to produce the required neutron performance, two compact liquid hydrogen moderators are located adjacent to the tungsten spallation target and must be supplied with less than 20 K hydrogen and a para hydrogen fraction of 99.8% or greater. The Cryogenic Moderator System (CMS) will consist of a single hydrogen loop feeding the two moderators in series cooled by a helium refrigerator with a cooling capacity of 2.5 kW at 17 K. The hydrogen loop consists of a hydrogen circulator, hydrogen helium heat exchanger, ortho-para converter, accumulator, transfer lines and heater. The design of the hydrogen loop is based on the CMS design of the First Target Station at the Spallation Neutron Source and some of the component designs may be reused. General hydrogen temperature control is provided by controlling the flowrate of helium to the heat exchanger. The hydrogen loop will have a constant flowrate of 0.5 L/s and remove a nuclear heat load of about 850 W from the two moderators, which is deposited both directly in the hydrogen and the adjacent hydrogen containing structures. Because the nuclear heat load is accelerator driven, the hydrogen system must remain stable when the heat load is removed instantaneously during beam trips. System stability is maintained passively with the accumulator and actively with the heater. Ionizing radiation which interacts with the liquid hydrogen drives backconversion of the hydrogen from parahydrogen to orthohydrogen. The STS moderator performance is very sensitive to small fractions of orthohydrogen requiring an ortho-para converter to maintain the hydrogen supplied to the moderators at near equilibrium parahydrogen concentration. STS CMS is in the early stage of preliminary design and current focus is evaluating component sizing and system stability during beam transients.

Research on dynamic prediction method of temperature environment of unmanned aerial vehicle platform based on physical information and neural network

During the flight of an unmanned aerial vehicle, the temperature environment inside the cabin is very harsh, resulting in frequent malfunctions of airborne equipment. Therefore, it is necessary to predict the temperature environment experienced by airborne equipment during its life cycle in the early stages of design to support the formulation of design requirements, determine test verification conditions, used and maintained. In order to accurately predict the temperature environment of the unmanned aerial vehicle platform, this paper proposes a prediction method that integrates physical information and neural networks. In view of the thermal environment characteristics of unmanned aerial vehicle platform, the physical mechanism of the formation and transfer of the unmanned aerial vehicle thermal environment was analyzed. Based on the physical mechanism equations and combined with the measured data of unmanned aerial vehicles, an Elman neural network model was established to achieve high flight speed, speed, etc. parameters are characterized as a function of the temperature inside the unmanned aerial vehicle cabin, and finally the model is verified. The results show that the temperature environment prediction model integrates physical information and neural networks can accurately predict the dynamic changes in the temperature inside the unmanned aircraft cabin. The maximum error of the model is 2.76°C, which meets engineering needs.