Early Design Phase Research Articles

Dynamic daylight performance oriented design optimizations for contemporary reading room represented deep open-plan spaces

Appropriate daylight environments are essential for library reading spaces that commonly meet long-term reading and writing. While conventional window form, position, and orientation designs felt increasingly difficult to comply the transformation towards deep, multi-functional open-plan modes of contemporary reading spaces. Insights of key design variable optimizations that can effectively enhance the dynamical daylight performance of open reading room represented deep open plans, therefore, become urgently needed. Employing dynamic daylight simulations validated by scaled-model measurements, this study firstly screened out the key design variables affecting both perimeter and deep areas through coupled orthogonal test-based sensitivity analyses. Afterwards, through daylight simulations considering differentiated illumination thresholds of both areas, single-factor analyses evaluating impacts of key design variables (window height and width coefficients, room heights, transmittances, and orientations) over both daylight intensities and glare probabilities have been thoroughly carried out. Qualified depths of both lighting threshold had been examined. The window width coefficient and transmittance were validated as critical design variables for perimeter area glare control. Window height and width coefficients, as well as room height were confirmed as main impacting factors to ensure mid-deep and deep zone illuminations, respectively. Moderately large window height (0.7∼0.8) and width (around 0.7) coefficients, glass transmittance (around 0.7) while moderately small room height (3.3∼3.6 m) are recommended. The research outcomes can assist in more effective decision-makings of early design phase daylight optimizations for common deep open-plan spaces. • Dynamical daylight simulations for varied depths among deep open-plans performed. • Orthogonal test based sensitivity analyses and single-factor analyses conducted. • Key variables for perimeter area glares and deep zone illumination levels validated. • Suitable design strategies of key variables for balanced daylight quality proposed.

A STUDY OF RESTORATIVE POTENTIAL IN WINDOW VIEWS ADOPTING KAPLAN’S ATTENTION RESTORATION THEORY AND URBAN VISUAL PREFERENCES DEFINED BY LYNCH

Window views can enable restorativeness. Previous studies define characteristics of natural environment that trigger restorativeness, but fewer studies are dealing with restorative characteristics of urban environment. The purpose of this study is to identify qualities that enable restorativeness in natural and urban window views. Using a questionnaire survey as a method for obtaining the research data, responses of architectural students are analysed. To identify restorative potential in window views evaluation method according to Kaplan’s Attention Restoration Theory (ART) is used. Additional evaluation method for urban views is applied using Lynch’s theory of designing urban space that people can enjoy. Our findings show that urban views can be analysed according to Lynch’s visual qualities to identify the restorative potential of urban environments. Our findings also show that Lynch’s criteria may coincide with Kaplan’s criteria for assessing restorativeness. To define restorative potential in urban views in an early design phase, new evaluation methods are needed to transcend the renowned Kaplan’s Attention Restoration Theory into more approachable design mechanisms for designers and decision makers. Well-being of the inhabitants in a future dense urban environment will only be provided with an adequate window view.

Decision-making under uncertainty in the early phase of building façade design based on multi-objective stochastic optimization

In response to the European 2030 climate and energy policy framework and to satisfy various stakeholders, trade-offs between different goals should be considered in the planning phase of a building. In the early phase, uncertainties are inevitable and should be modeled in the decision-making process. Especially for early modeling using building information modeling tools, it is of interest to find out appropriate “placeholder” materials. In this paper, we proposed a multi-objective stochastic optimization (MOSO) framework for decision-making in the early design phase of building façade design under uncertainty. Herein, two types of uncertainty were included: uncertainty in design decisions and environmental uncertainty. Coupled with a multi-criteria decision-making (MCDM) strategy, the framework aims to narrow down the possibilities of the material choice and to provide possibly robust solutions. Categorical and continuous parameters were considered to achieve three objectives: life cycle assessment in form of global warming potential, life cycle cost and thermal transmittance. Through a case study with solid timber and brick construction types, the proposed framework was validated. Results show that the insulation and outer wall cladding are the most varying parameters of the Pareto-optimal solutions for building façade design. In general, an environmental advantage in solid timber construction and an economic advantage in solid brick construction can be seen. With the established framework, decision-making considering dynamic changes during the planning process can be potentially realized in future works.

Analysis and modeling of the tip leakage flow on the performance of small-scale turbopumps for ORC applications

• An automated design model is developed to generate impeller geometries suitable for organic Rankine cycles. • The influence of tip clearance is investigated and modeled. • At high blade outlet angles, the effects of tip clearance and flow rate on the slip factor diminish. • The head rise decreases with the tip clearance ratio. • Feedforward neural networks are trained to model the slip factor and head loss coefficient of unshrouded centrifugal pumps. In this paper, the influence of tip clearance on the performance of small-scale turbopumps is studied numerically on extensive parameter ranges suitable for organic Rankine cycle applications. A novel and fully parameterized design model is developed and used to generate a wide range of turbopumps and their fluid domains to carry out three-dimensional computations across the impeller stage. Impellers are investigated at different operating conditions, and the accomplished results are analyzed to characterize the performance at design and off-design conditions. The CFD calculations demonstrate that the slip factor is dependent not only on its geometrical parameters, as considered by most correlations, but also on its operating conditions. The slip factor decreased almost linearly as the flow rate increased. In addition, the tip clearance ratio influences the slip factor, and its influence is non-linear. The head rise decreases as the tip clearance ratios increase. However, for radial impellers, a higher head rise was observed for small tip clearance ratios (<0.10). The numerical data is employed to infer reduced-order models for considering the tip clearance effect in the early-phase design process of small-scale turbopumps. The models predict the CFD data with an average relative deviation of 3.6 % and 5.8 % for the slip factor and the head loss coefficient, respectively.

BIM-based object mapping using invariant signatures of AEC objects

Building Information Modeling (BIM) can facilitate building engineering and performance analysis from the early design phase. Although BIM provides engineers with building objects and their information that can be used in a variety of platforms for creating analytical models, the shared objects have often lost track of their identification in different models during model creation, design iterations, and model transformation processes. To address this, mapping algorithms that can interface BIM and analytical models (e.g., building energy models (BEM), structural analysis models) were developed in this paper by automatically mapping objects based on their invariant signatures. An experiment was conducted on mapping space objects between BIM and BEM following the proposed framework. The developed algorithm was tested on a 4-story office building model with 82 spaces. The results showed that the algorithm achieved 90% precision and 90% recall for mapping space objects. A 4.88% improvement in the accuracy of energy simulation results was achieved through the algorithmic space mapping compared to that without space mapping. This research will contribute to filling informational gaps between BIM and analytical models. The proposed framework can also be used to further expand BIM interoperability support by providing a new data-driven approach for developing transformation algorithms.

Remote maintenance scenario of the beam line components of the ITER neutral beam system

The ITER neutral beam (NB) cell contains the neutral beam injectors (NBI) that provide heating power for fusion reaction in the tokamak machine. The NB cell is a radiation-controlled area where human worker access is strictly limited or prohibited depending on the NB cell configurations. Establishing the maintenance processes in the early design phase of a RH system is crucial in order to guarantee that the overall system configuration satisfies the maintenance requirements in given conditions and constraints. This paper describes the remote maintenance scenario of the beam line components (BLC) of the NB system by using the neutral beam remote handling system (NBRHS). This operation is a corrective maintenance operation that could happen multiple times during lifetime of ITER. If one of the BLCs fails, the failed NBI is isolated by closing the absolute valve to continue the tokamak operation. After that when the long-term maintenance campaign starts, the failed BLC needs to be replaced. The maintenance sequences involve human worker hands-on operations in the hot cell complex (HCC) and in the NB cell to prepare the RH operations, deployment operations of the NBRHS and the RH tools, removal/opening/transportation/storage operations to access to the failed component, and to remove and replace the failed component. Many RH tools are required during these processes such as unbolting/bolting, connector disconnection/connection, pipe cutting/welding/inspection, heavy and large component handling etc. Moreover, the operations need to be done without or minimizing spreading of contamination from the NBI vessels/components to the NB cell as the NB cell shall remain human accessible area during the long-term maintenance periods. The maintenance sequence study shows that the developed RH sequence is applicable in the current NB cell environment and is compatible with the defined interfaces in the plant systems. The developed scenario needs to be elaborated further detail in the next design phases together with the detail design of the RH equipment and the RH tools.

Integrated decision support for embodied impact assessment of circular and bio-based building components

The construction industry is accounted for one third of all waste generation in the European Union (EU) countries. The Interreg Circular Bio-Based Construction Industry (CBCI) project was conceived to explore the circular principles and bio-based materials for enhancing sustainability in construction industry. This study provides a new approach for decision support via a case study on early design phase of a demo terraced single family house; the living lab (LL) located in Ghent (Belgium). The decision support utilizes circularity tools and life cycle assessment (LCA) methodology. For this purpose, nine preliminary designs (PD) for the building envelope were assessed in a comparative study, which consists of three basic construction methods: masonry, light-weight steel and wood framing construction supplemented with bio-based materials. Environmental impact assessment was supported with the circularity tools focusing on the reuse and recyclability potential in the end-of-life (EoL) scenarios. The integration of LCA and circularity tools displayed that bio-based design PDs have an increased performance due to the avoided environmental impact through reuse and recycling in the EoL scenarios. As a result, a novel approach that integrates circularity calculations with LCA methodology was provided to improve long-term impact analysis on circular buildings with more precision in end-of-life scenario development.

EarlyData knowledge base for material decisions in building design

To make use of the great opportunities for emission reduction in early building design, future emissions need to be calculated when only geometric, but no detailed material information about a building is available. Currently, early design phase life cycle assessments (LCAs) are heavily reliant on assumptions of specific material choices, leading to single point emission values which suggest a precision not representative for an early design stage. By adding knowledge about possible locations and functions of materials within a building to life cycle inventory (LCI) data, the EarlyData knowledge base makes LCA data sets accessible and more transparent. Additionally, “generic building parts” are defined, which describe building parts independently of precise material choices as a combination of layers with specific functions. During evaluation, enriched LCI data and generic building parts enable assessment of a vast number of possible material combinations at once. Thus, instead of single value results for a particular material combination, ranges of results are displayed revealing the building parts with the greatest emission reduction potential. The application of the EarlyData tool is illustrated on a use case comparing a wood building and a concrete building. The database is developed with extensibility in mind, to include other criteria, such as (life cycle) costs.

EDU-AI: a twofold machine learning model to support classroom layout generation

PurposeThis study aims to present a twofold machine learning (ML) model, namely, EDU-AI, and its implementation in educational buildings. The specific focus is on classroom layout design, which is investigated regarding implementation of ML in the early phases of design.Design/methodology/approachThis study introduces the framework of the EDU-AI, which adopts generative adversarial networks (GAN) architecture and Pix2Pix method. The processes of data collection, data set preparation, training, validation and evaluation for the proposed model are presented. The ML model is trained over two coupled data sets of classroom layouts extracted from a typical school project database of the Ministry of National Education of the Republic of Turkey and validated with foreign classroom boundaries. The generated classroom layouts are objectively evaluated through the structural similarity method (SSIM).FindingsThe implementation of EDU-AI generates classroom layouts despite the use of a small data set. Objective evaluations show that EDU-AI can provide satisfactory outputs for given classroom boundaries regardless of shape complexity (reserved for validation and newly synthesized).Originality/valueEDU-AI specifically contributes to the automation of classroom layout generation using ML-based algorithms. EDU-AI’s two-step framework enables the generation of zoning for any given classroom boundary and furnishing for the previously generated zone. EDU-AI can also be used in the early design phase of school projects in other countries. It can be adapted to the architectural typologies involving footprint, zoning and furnishing relations.

Neural network meta–modelling for an efficient prediction of propeller array acoustic signature

In the framework of Simulation-Based-Design applied to aircraft, the use of meta–models provides an efficient and accurate alternative to demanding simulations or experiments, making it suitable for optimisation applications. In this context, the aim of this research is the development of a general methodology to be adopted since the earlier design phases, which can predict the acoustic emissions of Distributed–Electric–Propulsion systems, combining the accuracy of high–fidelity numerical tools with the computational efficiency of surrogate models. Here, the latter are obtained by means of Deep Neural Networks, using and comparing feed–forward and recurrent architectures. The numerical database used to train the surrogate models is obtained by an aerodynamic tool for potential-incompressible flows, followed by the application of the Farassat 1A boundary integral formulation for the evaluation of the noise field. The focus of the paper is on the development of surrogate models with different architectures, comparing them in terms of accuracy and computational efficiency, able to capture the effect of propeller geometric parameters and operative conditions changes on the acoustic emissions of a six counter-rotating propellers array configuration. The numerical results demonstrate that the surrogate models are capable to accurately predict the noise emissions of the multi-propeller configuration with a reduced computational cost.

A software tool for both the maximum tolerated dose and the optimal biological dose finding trials in early phase designs

Background:Phase I and/or I/II oncology trials are conducted to find the maximum tolerated dose (MTD) and/or optimal biological dose (OBD) of a new drug or treatment. In these trials, for cytotoxic agents, the primary aim of the single-agent or drug-combination is to find the MTD with a certain target toxicity rate, while for the cytostatic agents, a more appropriate target is the OBD, which is often defined by considering of toxicity and efficacy simultaneously. Accessible software packages to achieve both these aims are needed. Results:The objective of this study is to develop a software package that can provide tools for both MTD- and OBD-finding trials, which implements the Keyboard design for single-agent MTD-finding trials as reported by Yan et al. (2017), the Keyboard design for drug-combination MTD-finding trials by Pan et al. (2020), and a phase I/II OBD-finding method by Li et al. (2017) in a single R package, called Keyboard. For each of the designs, the Keyboard package provides corresponding functions such as get.boundary (⋯) for deriving the optimal dose escalation and de-escalation boundaries, select.mtd (⋯) for selecting the MTD when the trial is completed, select.obd (⋯) for selecting the OBD at the end of a trial, and get.oc (⋯) for generating the operating characteristics. Conclusion:The Keyboard R package developed herein provides convenient tools for designing, conducting and analyzing single-agent, drug-combination, phase I/II OBD-finding trials.

Ionization and current sheet formation in inductive pulsed plasma thrusters

Current sheet formation in inductive pulsed plasma thrusters (IPPTs) is investigated theoretically to determine how non-equilibrium ionization processes influence plasma impermeability to inductive electromagnetic fields and downstream propellant mass. Plasma impermeability to both electromagnetic fields and downstream mass is a prerequisite for efficient IPPT operation. A lumped-element circuit model of an IPPT plasma is modified to include propellant ionization and the electron energy balance under non-equilibrium conditions, neutral gas entrainment via charge exchange collisions, and electromagnetic coupling to a finite skin depth plasma. It is found that current sheets impermeable to both the accelerating fields and downstream mass—presumed to exist in all prior circuit modeling efforts—form only under specific conditions. The dynamics of electron heating during the early portion of the inductive current cycle are identified as the dominant contributors to current sheet formation. A new dimensionless scaling parameter is derived to characterize electron heating relative to inelastic ionization losses, from which it is found that impermeable current sheet formation requires Ohmic heating in the early formation phase to offset ionization losses associated with the entire propellant mass bit. This finding provides a physical explanation and generalization of the semi-empirical requirement on coil current rise rate that is commonly used in the early design phase of IPPTs to ensure current sheet formation.

Integrating Machine Learning with Parametric Modeling Environments to Predict Building Daylighting Performance

Buildings account for 30% of global energy consumption. To support the development of energy-efficient built environments and cities, architects, urban planners, and engineers have begun to utilize building performance simulation (BPS). Supporting decision-making and steering design toward high performance are crucial in the early design phase, when decisions have the biggest impact on the final product’s energy consumption and costs. Architects often assess the energy performance of hundreds of different design solutions and configurations in parametric environments to find the most energy-efficient solution. To adhere to strict project due dates, architects usually shorten the time required for BPS by reducing the number of variables or parameters for the building and facade design. This practice usually results in the elimination of design parameters that could potentially contribute to an energy-optimized design configuration. To achieve time-efficient BPS processes, recent research has focused on developing machine learning (ML) algorithms that can instantly predict the energy performance of buildings. Although ML has demonstrated high levels of accuracy, ML techniques are difficult to implement because they require technical knowledge of programming languages. Furthermore, these ML algorithms were developed as standalone applications, making them inappropriate for geometric modeling environments. This paper introduces a new ML tool that can be easily accessed within Grasshopper, a parametric modeling environment. The proposed tool can instantly predict the daylighting performance of building configurations.

Individualized standardization as the overarching principle in the context of planetary boundaries

To drive forward a fundamental change in the construction sector to achieve environmentally friendly progress in the construction processes, one promising approach is the synchronous implementation of the three main drivers technology, construction, and design. The key to this transformation is end-to-end digitalization. Our approach aims to achieve a new architectural quality by linking digitized design tools, ecological and environmentally friendly concepts, and serial production processes. Against this background, two research projects were advanced to enable adaptive prefabricated systems based on combined additive manufacturing. Two case studies illustrate the research results. In the first, a digital assessment tool is being developed to improve planning processes. With the tool, standardized yet adaptive constructions can be evaluated according to their sustainability impact in an early design phase. The second approach focuses on delivering a customized material composition realized with additive manufacturing methods. In an automated co-extrusion process, the dimensions and building-physical quality of story-high wall panels (floor-to-ceiling) can be adapted to individual requirements. The optimum balance of standardization vs individualized building solutions will introduce a novel flexibility for the architectural design process as well as a higher production efficiency. This strategy of individualized standardization is based on integrative collaboration and visionary research.

An artificial neural network tool to support the decision making of designers for environmentally conscious product development

The consideration of sustainability aspects in initial stages of product development is understood as an effective approach to plan a sustainable product life cycle. It can be achieved by integrating the sustainability considerations into decision making of companies in early design phases. This study aims to develop an Artificial Intelligence (AI) tool that can assist the designers in their decision making to choose environmentally benign design parameters of products. The proposed tool is based on an Artificial Neural Network (ANN) model which takes the life cycle design parameters (viz. size of product, density of material, manufacturing process, transport mode and recyclability) as inputs and provides the corresponding outputs in terms of ‘carbon footprint’ and ‘life cycle cost’ of a product. These outputs assist the designers to realize the trade-off between the environmental load and cost effectiveness of a design alternative. Thus, it enables the decision making of companies to select a more sustainable design. A Graphical User Interface (GUI) is developed for the AI tool so that the designers can efficiently use this tool. The results predicted by the proposed tool are compared with the results obtained through the life cycle assessment carried out by using GaBi 9.2. The comparison shows that the results predicted by the tool have a reasonable accuracy of more than 90% which is significant, especially in the design stages of environmentally conscious product development. Also, the time efficiency of the tool to compute the environmental impact was compared with that of GaBi 9.2 by using a T-test. Results showed that the time efficiency of the proposed AI tool is significantly higher than that of GaBi 9.2.

All-At-Once formulation integrating pseudo-spectral optimal control for launch vehicle design and uncertainty quantification

Launch vehicle design is a multidisciplinary process involving different disciplines such as aerodynamics, structure, propulsion and trajectory. This latter is a key discipline in the design process as it is used to assess the overall performance of the vehicle by solving of an optimal control problem involving a system of nonlinear ordinary differential equations. Usual Multidisciplinary Design Optimization methods for launch vehicle design consider the trajectory design discipline as an auxiliary embedded optimization problem to find the optimal trajectory control. In this paper, a multidisciplinary approach is proposed relying on an All-At-Once formulation by using Gauss–Lobatto collocation technique to solve the optimal control problem. The problem is solved using a gradient-based optimizer. Furthermore, based on this framework, an uncertainty quantification technique using post-optimality analysis is derived to perform sensitivity analysis. This allows to analyze the influence of modeling uncertainty on the optimal launch vehicle performance in the early design phases. The efficiency of the proposed approach is illustrated on the optimization and uncertainty quantification of a representative Two-Stage-To-Orbit launch vehicle design problem.

Evaluation of the Wind Catcher in the Traditional Cairene Courtyard Houses' Integrated Passive System for Natural Ventilation and Cooling

Many Islamic architectural features have been used continuously in Cairene Courtyard houses as one of the primary environmental standards. Wind catcher represents the keystone of passive green features among other used features. The response of chosen case studies to the thermal environment had proven to vary concerning the type of green features involved and its role in the whole integrated system. The evaluation matrix act as a model in assessing the performance of eco-architecture features regarding the type of integrated system. Wind catcher ended up being effective in expanding the efficiency of passive integration system for ventilation and cooling; especially when the opportunity is taken to involve other green features in the early design phase. It is recommended to take into consideration, the involvement of passive integrated systems in architectural projects. Accordingly, we can conserve energy, mitigate climatic changes, and achieve thermal comfort; besides preserving the local identity of the built environment.

A demonstration of sustainable pipeline routing optimization using detailed financial and environmental assessment

The selection of optimal pipeline routes should consider the costs for the design, construction, operation and maintenance of all the routing alternatives. The cost analysis of each alternative route requires substantial geospatial information on slopes and elevations, land use, soil types and other features. In addition to financial costs it is necessary to consider the sustainability aspects, including public safety and environmental impacts, if the pipeline passes near populated areas or if it crosses with natural habitats. This work uses an available computer-aided design tool to automate the routing optimization process. The optimal search follows Simulated Annealing to obtain a set of near-optimal pipeline routes and designs. This optimizer is able to consider a large variety of static geospatial features, conduct detailed hydraulic and cost calculations, and accommodate models for the evaluation of environmental impacts. Cost calculations are in accordance to widely recognized procedures used in pipeline design practice. For the quantification of environmental impacts, this study defines a cost function and proposes a simple implementation using geospatial data from CDDA and the Natura. The above are herein applied on the routing optimization of a crude oil pipeline, to demonstrate how financial and environmental criteria can be considered jointly at the early design phases of a real scale project. The pipeline is over 110 km long. The considered terrain involves areas of different land use, protected areas, cities and other infrastructures, seismic faults and Seveso plants. The data needed for these calculations are obtained from many geospatial data layers. The paper describes in detail the fusion procedures to pretreat the data and facilitate calculations of financial costs and environmental impacts. Results are obtained over a range of control variables to depict the trade-offs between the two criteria and other design options. The obtained results compare favorably with the routing proposed by the pipeline company.

Iceberg-seabed interaction evaluation in clay seabed using tree-based machine learning algorithms

In Arctic offshore regions, the oil and gas hydrocarbons are transferred to the onshore basins through the subsea pipelines. However, the operational integrity of the subsea pipeline may be at risk of collision with traveling icebergs, which gouge the seabed in the Arctic shallow waters. Even though these sea bottom-founded structures are buried at a secure depth below the seafloor, the pipeline is still threatened by the ice scouring event extended much deeper than the ice tip due to the shear resistance of the seabed soil. Modeling the sub-gouge soil characteristics is a challenging problem that requires costly experimental and long-running finite element (FE) simulations. To overcome these challenges, in this paper, the reaction forces and sub-gouge soil deformations in clay were modeled using an advanced extra tree regression (ETR) algorithm, as a quick and cost-effective alternative for the early design phases of pipeline engineering projects. Eight ETR models, ETR 1 to ETR 8, were developed by using the input parameters governing the iceberg-seabed interaction problem. The collected data were randomly split into 70% for training the machine learning (ML) models and 30% for testing purposes. The most accurate ETR models and the most significant input parameters were identified by performing a sensitivity analysis. The comparison of the most accurate ETR models and decision tree regression (DTR), random forest regression (RFR), and gradient boosting regression (GBR) algorithms proved that the ETR models had better performance to simulate the ice keel seabed interaction in clay.

Design Exploration of Bamboo Shells Structures by Using Parametric Tools

Bamboo is a promising material for structural use due to its mechanical properties and rapid growth. Currently, standards and codes to support the analysis and calculation of bamboo structures are still scarce. Even international standards do not include specifications for the design of complex geometries. This work aimed to verify the use of parametric tools to design complex shapes. First, the NSR-10 Colombian code was used to analytically solve and design the mechanical behavior of double-curved shells. Then, the results were compared to those calculated with the add-on Karamba3D. Lastly, verification was considered successful when both results coincided. Here, it should be noticed that the resultant data might lead to plastic deformation of the cylinder head. However, this work did not intend to formulate structural designs whose beams effectively support the requested stresses but focused on the verification of software applications. Once the software was verified, this work further analyzed the structural behavior of two double curvature geometries simulated with bamboo, showing that digital simulation tools have the potential to be applied during the early structural design phases.