Optimization Framework for Early Conceptual Design of Helicopters

Effective design collaboration during the early design stages in architecture is a condition for effective overall design and construction. This can be facilitated, supported and promoted through visual technological means. Collaborative design protocols implemented within the engineering fields (electrical and mechanical) can be adapted to the built environment design with emerging augmented reality technologies being the enablers for that shift. Architectural design requires a strongly visual approach to communication and means through which multiple disciples can collaborate effectively. Therefore, the aim of the research is to investigate principles and protocols to assist early design stage collaborative design among multidisciplinary teams, through visual and tactile user interfaces. This paper explores the current paradigm in AEC (architecture, engineering and construction), design collaboration and multidisciplinary group dynamics during the early design and conceptual stages in architecture. Afterwards, a design protocol is suggested, literature review regarding collaborative design and design protocols is summarized and further research is proposed on optimization and standardization of early design collaborative design in AEC.

Supersystem digital twin-driven framework for new product conceptual design

Multivariate regression as an energy assessment tool in early building design

This study estimated availability of an air compression system and a nitrogen generation system in liquefied natural gas—floating production storage and offloading unit (LNG-FPSO) with different design stages to investigate the gap between the availability at the early design stage and that at the late design stage. Although availability estimation in the early design stage is more important than the late design stage, it is difficult to estimate the availability accurately in the early design stage. The design stage was divided into three depending on the design progress. Monte Carlo simulation technique was employed for the availability estimation. The results of the availability estimation showed that there was 0.434% difference between the early and late design stages. This meant that the availability in the early design stage was underestimated due to limited information. A sensitivity analysis was performed to investigate critical factors affecting the results. The investigated factors were failure rate, repair time, redundant equipment, and modified preventive maintenance schedule. The most critical factor was redundant equipment. It increased 0.486% availability.

Buildings account for nearly 40% of the global process and industrial greenhouse gas emissions, and within the building sector. And within the building sector, nearly 72% of the CO2 emissions are due operational carbon demand in the building sector. To meet the goals set out in the Paris Accord, we need to halve carbon emissions by the end of this decade: an aggressive and ambitious timeline. It has become an immediate and urgent need to design and construct buildings that are minimally intrusive in their impact on the environment and operate efficiently. Improvement of the building's energy performance must begin at the early design stage as the potential for sustainable intervention in the project's early phases is higher. The opportunity to improve building performance as the design progresses, is constantly reducing, while the cost of optimization is constantly increasing. One of the major design variables impacting the performance at the early phase is the building morphology/form and its associated variables. Sensible optioneering of the building form and its variables at the early stages of design - especially the conceptual design stage - can help improve the performance of the design early on in the design process at minimum cost. However, for effective early-phase design optimisation, there is a need to develop tools/methods that allow instantaneous evaluation of a large design sample space without much “lag” between the design and performance evaluation workflows. Also, in regular early phase sustainable design workflows, multiple stakeholders - like the designer, the performance analyst, etc. - are involved, which leads to a cognitive divide in the design process. For instance, while the architect designs the initial conceptual massing with a functional design intuition, the performance analysts suggest design interventions with a performance oriented design intuition. This cognitive divide leads to a dipartite design cognition in early phase design. The underlying premise of this research is to support the notion of blurring the dichotomy between conflicting design intuitions through exploring Human + Artificial Intelligence (AI) synergies and their underlying foundational technological requirements with respect to early phase design, to enable intuitive high performance design scenarios with a centralized design cognition where there are no other stakeholders other than the designer and the design environment. With the paradigm shift of other industries towards Machine Learning (ML) and AI, recent research advances in ML and the increasing availability of Big-data in the AEC industry have bolstered multivariate problems like building performance evaluation to a great extent. Design optioneering with instantaneous performance feedback through a ML method called Surrogate Modelling is an upcoming and promising methodology, which delivers feedback based on knowledge through available data, rather than simulation. Also, paradigms in Concurrent Human Machine Interaction (HMI) have explored avenues of augmenting the architectural design process through data-driven approaches. The type of data (input-output pairs) forming the basis for these paradigms depend on the target problem at hand. For early phase performance optimization - especially energy use optimization - Building Energy Model (BEM) forms the primary data. The US Department of Energy defines BEM as a versatile, multipurpose tool that is used in new building and retrofit design, code compliance, green certification, qualification for tax credits and utility incentives, and even real-time building control. BEM is also used in large-scale analyses to develop building energy-efficiency codes and inform policy decisions. A comprehensive BEM dataset that is large and accessible can support the growing interest in ML and HMI research in the high performance building design field. However, common techniques to acquire large BEM datasets like manual 3D energy modeling and simulation, 3D scanning, etc. are very tedious and time consuming. Also, existing datasets are usually incomplete, inconsistent and very difficult to access, owing to data privacy and open-access issues. And lastly, these datasets are fixed, static and cant be easily reproduced for different use case scenarios, nor scalable to adapt to needs. This research aims to tackle this problem by introducing a novel framework to generate custom problem-specific synthetic BEM dataset that generates a user defined amount of context specific 3D early phase building geometries and their associated BEM models, that is suitable for ML and HMI research. Synthetic datasets generated with this framework offer flexibility and customization in the generation process, making these datasets modular, reproducible and scalable. The framework uses the concepts of Generative Design, Geometry Manipulation, Simulation and Computational Automation to build the dataset. The dataset is qualified through the concepts of Preservationism and Sustainability, which are discussed in further sections of this article. The research identifies the importance and multifacetedness of the impact of building geometry on its performance, and demonstrates the application of generated synthetic BEM datasets by developing an enactive, conversational design environment that allows the designer to make real-time sustainable design decisions based on instantaneous machine feedback, for an intuitive, centralized design intuition.

Early stage design for an institutional net zero energy archetype building. Part 1: Methodology, form and sensitivity analysis

Life Cycle Assessment (LCA) is used and accepted as a method to assess environmental impacts and resource use of buildings. In practice, LCA is typically used in stages where the design of the building is already finalized. However, LCA-calculations from early design stages can be used actively in design and optimization of the building. One of the obstacles to early stage LCA is that extensive data input on precise material types and amounts is needed, which is limited in early design stages. The simplifications needed for a designer in an early design LCA is addressed in a research project, where an extensive library of predefined building components and installations were developed and integrated into the existing Danish LCAbyg tool. The library assists the user in establishing a full building inventory by simple inputs of geometry of the building and a selection from the library of building element layers. However, the simplified approach to LCA of a building at early design stages inevitably affects results compared with results of a calculation made at later design stages where more, specific data is available. This paper presents an evaluation of building cases, modelled with the same background database and life cycle stages, using the simplified early design LCA approach and a detailed LCA approach. The evaluation includes testing of how well the predefined components in the early design approach fit with the case buildings and comparisons of the total material input and precision of the final LCA results.

The motivation behind the CDP interdisciplinary research project is to resolve the current discrepancy between familiar, analogue ways of working in the early architectural design stages and the ever increasing use of digital tools in office practice. The project’s objective is the conception and prototypical realisation of an interactive work environment for use in the early design phases. By directly linking familiar analogue ways of working with digital computer aided design tools, the CDP represents a working environment that allows designers to work the way they are used to while making use of the potential of computers. This paper describes the first results of a design environment for supporting the conceptual phase of urban design.

TopoBIM is a 3D web-based viewer for BIM data that facilitates the capture of stakeholder knowledge related to project requirements and constraints in early design. The software provides an interface for viewing 3D models and data for selected room types and adding topological annotations. The use of topological representation is proposed as a method for facilitating knowledge capture, allowing decisions about the details of plan layout to be deferred and widening the potential for participation in the early stage design process. Topological representation is widely employed in the engineering disciplines, but is not commonly used as a means of capturing schematic information in early design. TopoBIM is proposed as a methodology and workflow for the introduction of topology in early design, and as an example of lightweight, bespoke software that informs design by allowing stakeholders to perform specific tasks using BIM data, without the constraints and limiting complexity of BIM authoring environments.

ABSTRACTWhile extensive modelling - both physical and virtual - is imperative to develop right-first-time products, the parallel use of virtual and physical models gives rise to two interrelated issues: the lack of revision control for physical prototypes; and the need for designers to manually inspect, measure, and interpret modifications to either virtual or physical models, for subsequent update of the other. The Digital Twin paradigm addresses similar problems later in the product life-cycle, and while these digital twins, or the “twinning” process, have shown significant value, there is little work to date on their implementation in the earlier design stages. With large prospective benefits in increased product understanding, performance, and reduced design cycle time and cost, this paper explores the concept of using the Digital Twin in early design, including an introduction to digital twinning, examination of opportunities for and challenges of their implementation, a presentation of the structure of Early Stage Twins, and evaluation via two implementation cases.

For safety critical complex systems, reliability and risk analysis are important design steps. Implementing these analyses early in the design stage can reduce costs associated with redesign and provide important information on design viability. In the past several years, various research methods have been presented in the design community to move reliability analysis into the early conceptual design stages. These methods all use a functional representation as the basis for reliability analysis. This paper asserts that, in non-nominal system states, the functional representation limits the scope of failure analysis. Specifically, when failures are modeled to propagate along energy, material, and signal (EMS) flows, a nominal-state functional model is insufficient for modeling all types of failures. To capture possible failure propagation paths, a function-based reliability method must consider all potential flows, and not be limited to the function structure of the nominal state. In this light, this paper introduces the Flow State Logic (FSL) method as a means for reasoning on the state of EMS flows that allows the assessment of failure propagation over potential flows that were not considered in a functional representation of a “nominally functioning” design. A liquid fueled rocket engine serves as a case study to illustrate the benefits of the methodology.

Although the product life cycle cost (LCC) is mainly committed by early design stages, designers do not consider the costs caused in subsequent phases of life cycle. The estimating method for the product life cycle cost in early design processes has been required because of both the lack of detailed information and time for a detailed LCC for a various range of design concepts. This paper suggests an approximate LCC method that allows the designer to make comparative LCC estimation between the different product concepts. The product attributes at the conceptual design phase and LCC factors are introduced and the significant product attributes are determined by statistical analysis. Neural network algorithms are applied to estimate LCC by considering the identified product attributes as inputs and the LCC as output. Trained learning algorithms for the known characteristics of existing products will quickly give the estimation of LCC for new product concepts. The estimation for maintenance and energy costs of electronic appliances is shown as an example. The proposed method provides the good estimation for the LCC and gives the guidelines leading to cost-effective design decision-making at the early design stage.

Hand-done design drawing still has a several advantages over current, CAD-based approaches to generating form, especially in the early stages of design. One advantage is the indeterminacy of hand drawings—i.e. its abstractness and ambiguity. Another is a non-destructive drawing process, where new drawings are created without modifying old ones. A third is designers’ creation of large collections of inter-related drawings—i.e. graphical hyperdocuments. A fourth is the unobtrusive character of conventional drawing tools. These advantages might be taken as reasons for continuing to do early design on paper, but they also suggest ways in which CAD might be improved. We have created software prototypes that incorporate these features into a new type of CAD based on sketching with electronic pens on LCD tablets. One prototype, which we call HyperSketch II, simulates tracing paper in the sense that it enables the user to trace over previous drawings and to build stacks of traced over drawings. It also enables the user to create a hypermedia network in which the nodes are sketches and the links represent various relationships between sketches.

PurposeBuildings are major contributors to greenhouse gases (GHG) along the various stages of the building life cycle. A range of tools have been utilised for estimating building energy use and environmental impacts; these are time-consuming and require massive data that are not necessarily available during early design stages. Therefore, this study aimed to develop an Environmental Impacts Cost Assessment Model (EICAM) that quantifies both energy and environmental costs for residential buildings.Design/methodology/approachAn Artificial Neural Network (ANN) was employed to develop the EICAM. The model consists of six input parameters, including wall type, roof type, glazing type, window to wall ratio (WWR), shading device and building orientation. In addition, the model calculates four measures: annual energy cost, operational carbon over 20 years, envelope embodied carbon and total carbon per square metre. The ANN architecture is 6:13:4:4, where the conjugate gradient algorithm was applied to train the model and minimise the mean squared error (MSE). Furthermore, regression analysis for the ANN prediction for each output was performed.FindingsThe MSE was minimised to 0.016 while training the model. Also, the correlation between each ANN output and the actual output was very strong, with an R2 value for each output of almost 0.998. Moreover, validation was conducted for each output, with the error percentages calculated at 0.26%, 0.25%, 0.03% and 0.27% for the annual energy cost, operational carbon, envelope materials embodied carbon and total carbon per square metre, respectively. Accordingly, the EICAM contributes to enhancing design decision-making concerning energy consumption and carbon emissions in the early design stages.Research limitations/implicationsThis study provides theoretical implications to the domain of building environmental impact assessment through illustrating a systematic approach for developing an energy-based prediction model that generates four environmental-oriented outputs, namely energy cost, operational energy carbon, envelope embodied carbon, and total carbon. The model developed has practical implications for the architectural/engineering (A/E) industries by providing a useful tool to easily predict environmental impact costs during the early design phase. This would enable designers in Saudi Arabia to make effective design decisions that would increase sustainability in the building life cycle.Originality/valueBy providing a holistic predictive model entitled EICAM, this study endeavours to bridge the gap between energy costs and environmental impacts in a predictive model for Saudi residential units. The novelty of this model is that it is an alternative tool that quantifies both energy cost, as well as building’s environmental impact, in one model by using a machine learning approach. Besides, EICAM predicts its outcomes more quickly than conventional tools such as DesignBuilder and is reliable for predicting accurate environmental impact costs during early design stages.

Whole-life carbon emissions (WLCE) studies are critical in assessing the environmental impact of buildings and promoting sustainable design practices. However, existing methods for estimating WLCE are time-consuming and data-intensive, limiting their usefulness in the early building design stages. In response to this, this research introduces a novel approach by harnessing various machine learning algorithms to predict WLCE and WLCE intensity (normalised by floor area) for buildings. To evaluate the suitability of machine learning algorithms, we conducted an experiment involving ten algorithms to build the prediction models. These models were trained using data from 150 typical residential properties in Cornwall, UK, along with 28 features obtained from a comprehensive survey, including floor area, heating type, and occupant characteristics. The ten algorithms include Multiple Linear Regression, and non-linear algorithms such as Decision Tree, Random Forest. Performance evaluation metrics, such as coefficient of determination (R2), mean absolute error (MAE), means squared error (MSE), root-mean-square error (RMSE), and elapsed time, were employed. Our research contributes to the field by showcasing the effectiveness of machine learning models in predicting building WLCE. We reveal that all the tested machine learning algorithms have the capability to predict WLCE and WLCE intensity, non-linear models outperform linear ones, and the Random Forest (RF) model demonstrates superior performance in terms of accuracy, stability, and efficiency. This research encourages the integration of life cycle studies into the early design stage, even within tight building design schedules, offering practical guidance to architects and designers. Furthermore, these results also benefit a wide range of stakeholders, not only the architects but also the engineers, policymakers, and life cycle assessment (LCA) researchers, contributing to the advancement of data-driven sustainability approaches within the building sector.