Early Design Phase Research Articles

A Visco-Elasto-Plastic Constitutive Law for Deformation Prediction of High Concrete Face Rockfill Dams

Deformation predictions in high Concrete Face Rockfill Dams tend to underestimate observed settlements due to scale effect and breakage phenomena that cannot be adequately captured by laboratory tests. This paper presents a Visco-Elasto-Perfectly Plastic (VEPP) model for predicting deformations in high Concrete Face Rockfill Dams (CFRDs) that addresses these challenges incorporating explicitly key rockfill parameters like grain size and post-compaction porosity, which influence both the non-linear elastic and plastic behaviors of rockfill. The VEPP model enables deformation prediction while using standard laboratory test results. The model’s effectiveness was demonstrated through its application to the 233 m high Shuibuya Dam, the tallest CFRD in the world. The VEPP model predictions closely align with observed deformations throughout the dam’s construction, impoundment, and early operational stages. By using physically meaningful parameters, the model reduces the uncertainty associated with the empirical assessment of model parameters using back-analysis from similar projects. While the VEPP model offers improved predictive accuracy, particularly during early design phases, further advancements could be achieved by refining the creep formulation and accounting for grain size evolution during construction. This approach has the potential to optimize the design and construction of future high CFRD construction.

Model‐based systems engineering and safety assessment: A workflow for mechatronic systems design

AbstractMechatronic systems become ever more complex because of their increasing number of interconnected safety critical components and sophistication. MBSE (Model‐based Systems Engineering) and MBSA (Model‐Based Safety Assessment) are the most commonly adopted approaches to deal with the design and safety analysis of mechatronic systems. Unfortunately, both approaches are normally adopted separately, especially in the earlier phases of system design, thus leading to a lack of communication between system engineers and the safety team. This work aims to fill that gap at a high level, that is, through process interaction. This paper proposes an enhanced V‐model for the design of safety‐critical mechatronic systems. It relates a system development process with specific safety assessment methods. Specifically, the proposed workflow details exchange flows between the RFLP (Requirements, Functional, Logical, Physical) method, the FHA (Functional Hazard Analysis), the FMEA (Failure Mode and Effects Analysis), the MBSA and simulation, and the FTA (Fault Tree Analysis). These analyses are complemented with multiphysics modeling and simulation to observe system behavior in functional and failure scenarios, with the aim of requirements verification. The design workflow has been applied to a winged Unmanned Aerial Vehicle to apply the parallel process and the necessary interaction of MBSE and MBSA approaches. The information flows between the individual activities proved effective for designing a safe system before the verification phase. The main benefit of the proposed workflow is providing both the design and safety team with some interaction points, thus avoiding a lack of safety‐critical analysis in the early phases of system design.

Artificial Neural Network to Predict Curvature Light Shelf Design Related Daylighting Optimization on Office Spaces

Energy Optimization in building design field now has been revolutionized due to AI and machine learning applications. Leveraging daylight to reduce artificial lighting consumption holds promise for significant energy savings, yet the nonlinear nature of daylight patterns poses challenges in prediction and optimization. This study proposes a novel approach to automated light shelf design using machine learning algorithms, specifically artificial neural networks (ANNs) such as recurrent neural networks (RNN) by long short term memory (LSTM), to accelerate daylighting simulation and optimization processes. The methodology employed two distinct approaches: Firstly, we employed the theoretical-analytical approach to explore methods for utilizing machine learning in natural lighting and light shelf parameters. Second, the practical and applied method involved creating a predictive model for designing the light shelf using appropriate AI and ML techniques. This model is based on an office geometry model at the El Arish weather file in Egypt, four-dimensional training room models with three internal zones-oriented south. Rhinoceros and Grasshopper, two parametric simulation tools, are used to normalize and optimize light shelf parameters like geometry cross-section, curvature surface, width, height position, depth, tilt angle, and reflectance materials. Then, the Galapagos plug-in and Colibri2 are used for dataset creation and optimization. The results demonstrate that automated light shelf operation has a significant impact on internal daylighting quality. RNNs enable rapid prediction of optimization models, reducing time consumption in the early design phase. ML facilitates decision-making by generating evaluative criteria from user-selected design options. RNNs were classified as good and bad and used LSTM to enhance prediction accuracy for efficient illuminance values metric in zones 1 and 2. Challenges include increasing the simulation procedure's efficiency. The results of model accuracy reached 99%. Hence, future research should prioritize resolving the previously identified concerns. In conclusion, this study underscores the importance of ML-driven approaches in early design phases to optimize building energy consumption and pave the way for sustainable architectural practices.

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.

Acausal Fuel Cell Simulation Model for System Integration Analysis in Early Design Phases

Hydrogen technologies have the potential to reduce aviation’s CO2 emissions but come with many challenges. This paper introduces a scalable hydrogen fuel cell model tailored for system integration analysis in early aircraft design phases. The model focuses on Proton Exchange Membrane Fuel Cells (PEMFCs) and is based on thermodynamic equations and empirical data to simulate performance under different ambient and operating conditions; it also includes a simplified model of the Balance of Plant (BOP) systems and is implemented in OpenModelica. The model performance is validated through a comparison of the simulated polarization curves with real datasheet data. A case study highlights the peculiarities of this model by studying the sizing of the fuel cell stacks for a modified ATR 72 aircraft. The developed model effectively supports the early design exploration of the aircraft with a greater level of detail for system integration studies, essential to better explore the potential of aircraft featuring hydrogen-based power systems.

Parametric Cost Prediction Models for Light General Aviation Aircraft in the Early Design Phase

Parametric Cost Prediction Models for Light General Aviation Aircraft in the Early Design Phase

Multi-View Graph Learning for Path-Level Aging-Aware Timing Prediction

As CMOS technology continues to scale down, the aging effect—known as negative bias temperature instability (NBTI)—has become increasingly prominent, gradually emerging as a key factor affecting device reliability. Accurate aging-aware static timing analysis (STA) at the early design phase is critical for establishing appropriate timing margins to ensure circuit reliability throughout the chip lifecycle. However, traditional aging-aware timing analysis methods, typically based on Simulation Program with Integrated Circuit Emphasis (SPICE) simulations or aging-aware timing libraries, struggle to balance prediction accuracy with computational cost. In this paper, we propose a multi-view graph learning framework for path-level aging-aware timing prediction, which combines the strengths of the spatial–temporal Transformer network (STTN) and graph attention network (GAT) models to extract the aging timing features of paths from both timing-sensitive and workload-sensitive perspectives. Experimental results demonstrate that our proposed framework achieves an average MAPE score of 3.96% and reduces the average MAPE by 5.8 times compared to FFNN and 2.2 times compared to PNA, while maintaining acceptable increases in processing time.

Assessment of Multifidelity Surrogate Approaches for Expedient Loads Prediction in High-Speed Flows

Fast and accurate predictions about the flowfield surrounding a hypersonic flight vehicle are necessary for both early design phases and autonomous vehicle control. Data-driven model reduction presents an opportunity to harness the accuracy of high-fidelity techniques for expedient online application. Surrogate modeling is one approach that seeks to emulate the response of a system with evaluation times much faster than the data source. Multifidelity surrogate modeling seeks to reduce the amount of high-fidelity data needed for a sufficiently accurate online model. The objective of this paper is to assesses the tradeoffs between a kriging-based multifidelity model and a neural-network-based multifidelity model, applied to high-speed flow past a canonical flight geometry. All models studied in this work reproduce the results of the benchmark simulations with less than 100 training cases. The kriging models marginally outperform the neural-network-based models in accuracy, but requirements for training and storing the kriging models scale exponentially as the training data gets larger. Kriging models offer built-in uncertainty quantification and require significantly less decision making about hyperparameters and architecture choices. This study also indicates that the inclusion of data from classical engineering methods can improve the prediction accuracy of surrogate models at practically no cost.

Optimizing the cost of the STEP programme.

The Spherical Tokamak for Energy Production (STEP) programme is a world-leading fusion power plant programme that has embedded a cost conscience in its design from the early phases. This firmly addresses the attitude of cost complacency of which many major infrastructure projects have historically been accused. While a detailed and highly accurate whole life cycle cost analysis is not possible, or even valuable, during the conceptual design stage, this early design phase is still the most critical programme phase where a focus on costs can drive longer term reductions and impact whole life cycle costs at the high level. Consequently, appropriate estimating methods for these early-stage designs and lessons learned from other industries are used to inform design decisions and ensure cost is part of the overall option analysis. Hence, while the overall programme cost estimate is too immature to be a reliable indicator for the final programme costs, significant effort has been undertaken to understand the major cost drivers and take action to make the STEP design as cost-effective as possible. This article is part of the theme issue 'Delivering Fusion Energy - The Spherical Tokamak for Energy Production (STEP)'.

Formal Methods in Industry

Formal methods encompass a wide choice of techniques and tools for the specification, development, analysis, and verification of software and hardware systems. Formal methods are widely applied in industry, in activities ranging from the elicitation of requirements and the early design phases all the way to the deployment, configuration, and runtime monitoring of actual systems. Formal methods allow one to precisely specify the environment in which a system operates, the requirements and properties that the system should satisfy, the models of the system used during the various design steps, and the code embedded in the final implementation, as well as to express conformance relations between these specifications. We present a broad scope of successful applications of formal methods in industry, not limited to the well-known success stories from the safety-critical domain, like railways and other transportation systems, but also covering other areas such as lithography manufacturing and cloud security in e-commerce, to name but a few. We also report testimonies from a number of representatives from industry who, either directly or indirectly, use or have used formal methods in their industrial project endeavours. These persons are spread geographically, including Europe, Asia, North and South America, and the involved projects witness the large coverage of applications of formal methods, not limited to the safety-critical domain. We thus make a case for the importance of formal methods, and in particular of the capacity to abstract and mathematical reasoning that are taught as part of any formal methods course. These are fundamental Computer Science skills that graduates should profit from when working as computer scientists in industry, as confirmed by industry representatives.

To draw or not to draw? Evaluating the impact of computer-aided drawing tools on productivity in architectural design

This empirical study investigates the architectural design process, focusing on the influence of computer-aided drawing (CAD) tools during the initial design stages. Given the increasing reliance on CAD tools in contemporary architectural practice, the primary objective is to assess quantitatively how CAD tool adoption affects design productivity. The research was conducted in two phases, utilizing the protocol analysis method. In the first phase, we introduced a novel approach by having third-year architecture students complete two design assignments: the first using traditional freehand sketches and the second employing CAD tools. This unique method allowed us to capture their design actions and thoughts using the Concurrent verbalization technique. In the second phase, a meticulous comparison of the two sets of design protocols was undertaken. The analysis yielded surprising and counterintuitive results. Contrary to common beliefs, the use of CAD tools during early design phases can lead to unintended outcomes. Despite their digital precision and convenience, these tools may inadvertently impede cognitive productivity. Designers employing CAD tools showed signs of hesitation and indecision, resulting in prolonged design timelines and diminished ideational productivity. These findings have profound implications for architectural education and practice. They underscore the necessity for a balanced approach to integrating CAD tools into design processes. Educators and professionals must carefully consider the potential impact of these tools on the creative process, prompting a reassessment of their role in architectural design curricula and workflows. Future research holds great promise in exploring strategies to optimize CAD tool usage to mitigate their disruptive effects on early-stage architectural design. Additionally, investigating the role of training and interface design in enhancing the synergy between designers and digital tools offers exciting and fruitful research directions. In summary, this study highlights the complex relationship between CAD tools and cognitive productivity in architectural design, prompting a reexamination of their role in shaping the design landscape.

From intra- to extra-uterine: early phase design of a transfer to extra-uterine life support through medical simulation.

Extra-uterine life support technology could provide a more physiologic alternative for the treatment of extremely premature infants, as it allows further fetal growth and development ex utero. Animal studies have been carried out which involved placing fetuses in a liquid-filled incubator, with oxygen supplied through an oxygenator connected to the umbilical vessels. Hence, by delaying lung exposure to air, further lung development and maturation can take place. This medical intervention requires adjustments to current obstetric procedures to maintain liquid-filled lungs through a so-called transfer procedure. Our objective was to develop obstetric device prototypes that allow clinicians to simulate this birth procedure to safely transfer the infant from the mother's uterus to an extra-uterine life support system. To facilitate a user-centered design, implementation of medical simulation during early phase design of the prototype development was used. First, the requirements for the procedure and devices were established, by reviewing the literature and through interviewing direct stakeholders. The initial transfer device prototypes were tested on maternal and fetal manikins in participatory simulations with clinicians. Through analysis of recordings of the simulations, the prototypes were evaluated on effectiveness, safety and usability with latent conditions being identified and improved. This medical simulation-based design process resulted in the development of a set of surgical prototypes and allowed for knowledge building on obstetric care in an extra-uterine life support context.

Meta learning regression framework for energy consumption prediction in retrofitted buildings: A case study of South Korea

Building energy consumption (BEC) prediction is crucial for optimizing energy efficiency in building retrofit projects, particularly during the early design phase. However, existing machine learning (ML) models often face challenges with overfitting and complexity, limiting their accuracy and generalizability. This study addresses these issues by proposing a novel meta-learning regression framework for energy prediction in green retrofitted buildings. The framework employs optimized base regressors using various ML models and integrates them through stacking regression to enhance predictive accuracy and generalization. The model’s performance is evaluated against advanced methods like Deep Neural Networks and Ensemble Learning Regression using data from over 811 green retrofitting projects in South Korea. The proposed method demonstrates superior efficiency, achieving the lowest mean absolute error (19.899 for Primary Energy Consumption and 11.301 for Energy Required Amount), lowest root mean squared error (29.494 for Primary Energy Consumption and 19.977 for Energy Required Amount), and highest R-squared score (0.786 for Primary Energy Consumption and 0.542 for Energy Required Amount). This research contributes a novel approach to ensemble modeling for BEC prediction, showcasing its superior accuracy, generalizability, and practical applicability in the context of green building retrofits.

Nurse Involvement in Health Information Technology Design for Computerized Nursing Practice: A Scoping Review.

This scoping review assesses evidence regarding nurse involvement in health information technology (health IT) design, focusing on the method(s), frequency, capacity, and levels of involvement. The JBI methodology for scoping reviews was used to search seven multidisciplinary databases, yielding 2948 articles. After screening, 98 articles were included for data abstraction. Textual data summary is ongoing. Preliminary findings highlight that nurses are often involved in the late stages of health IT design, with less frequency in the early and pre-programming design phases. Most studies used a user-centered design approach to elicit nurses' views about health IT tools after the tools had been developed, with nearly half being point of care nurses. Increasing nurse involvement in health IT design may help to improve nurses' perceptions of health IT that nurses use.

Construction safety challenges and opportunities at design stage for building industry in Pakistan

The implementation of Design for Construction Safety (DfCS) at the early design phase can proactively identify and address potential hazards in construction projects. By integrating safety considerations into the design process, it becomes possible to eliminate or minimize risks before they become costly and dangerous issues can also be avoided during construction. To gain insights into the current practices and barriers faced by professionals in the construction industry of Pakistan regarding DfCS, a comprehensive study was conducted. This study utilized a combination of questionnaire-based surveys and interviews to gather data from various stakeholders involved in the design and construction processes. The research revealed that DfCS was absent in the standard design practice. The weaknesses identified for the implementation of DfCS were designers’ inevitable doubts about increasing cost, schedule problems, absence of contracts and regulations, designers’ lack of safety knowledge and skills. The largest influential factors were regulatory liability on the designer (29%) and increasing costs (27%). The study identified that less trained worker were rated 59% for site accidents. 87% of respondents suggested revision of contract and regulations. The study proposed a framework to check constructability with respect to safety standards. Alternative project delivery method i.e., Design Build (DB) was recommended to reduce health and safety accidents and barriers.

Maintainability Assessment during the Design Phase: Integrating MTA and UNE 151001

The focus on maintenance actions in the early design phases has been a trend in recent years. The main sources of information during the design of the maintainability process include operator reports, maintainer experience, failure history, and manufacturer recommendations. During this process, an important aspect is related to the configuration of maintenance tasks and interventions, such as their main phases, activities, and durations. The allocation or estimation of maintainability involves identifying and/or estimating the mean time to repair (MTTR) for each component or system. The time of the maintenance tasks or the repair time are fundamental for companies, as the availability of equipment directly depends on this parameter. In this study, a new method for evaluating maintainability during the design phases is proposed. The method is based on the integration of the maintenance task analysis (MTA) principles and the UNE 151001 maintainability evaluation standard. A data structure is proposed that serves the application of the UNE151001 procedures, obtaining a data-based maintainability evaluation. As a validation procedure, an application of the proposed approach is presented using two overhead cranes. Comparisons and recommendations are made regarding the maintainability of both pieces of equipment. Finally, some managerial and engineering insights are presented.

A holistic approach for efficient greener in-space propulsion

This paper presents a comprehensive framework for designing in-space propulsion systems, integrating four criteria: global propulsive performance, environmental impact, cost efficiency, and architectural reliability. The study focuses on the emerging class of Orbital Transfer Vehicles to illustrate the application of this method. By examining the synergistic potential of OTVs and greener propellants, the paper addresses different mission scenarios, including LEO, GEO, and lunar missions, with both scientific and commercial objectives. The proposed framework aims to go beyond traditional cost-centric approaches, offering a more complete evaluation method for early design phases. A case study comparing three liquid bipropellant options, pressure-fed MON-3/MMH, 98%-HTP/RP-1, and self-pressurizing N2O/Ethane, demonstrates the utility of the tool. Findings suggest that scientific missions benefit most from 98%-HTP/RP-1, while traditional propellants remain preferable for cost-driven commercial missions to GEO and the Moon, though greener alternatives are competitive for less demanding LEO missions. This innovative framework aims to guide the selection of propulsion systems to achieve greener space missions, aligning traditional performance figures with environmental responsibility.

A novel metric for assessing structural complexity of data warehouse requirements models

Metrics can be used to evaluate the overall structural complexity of the data warehouse (DW) and assess its quality in the early phases of design and development. The requirements data model plays a crucial role in developing a quality-based DW. Several researchers have assessed the quality of the requirements data model using the requirements metrics based on the agent-goal-decision-information (AGDI) model, both formally and empirically. However, no assessment has been implemented on the structural complexities of the requirements data model based on goal, decision, and information hierarchies. Considering this, the study proposes a novel structural complexity metric to assess the overall complexity of the requirements data model at different phases (information, decision and organisation) and to analyze the links between the different levels of granularity in these models. The proposed metric is formally validated by applying the measurement theory-based Briand’s framework for complexity measure (i.e., one of the five notions of measurements in Software Engineering) by confirming that the proposed metric is valid and defined correctly under certain mathematical properties. As a result, we conclude that the proposed structural complexity metric is accurately defined and effectively valid. We also provide a detailed comparison of various requirements metrics commonly used in similar contexts to contextualize our proposed metric within the broader landscape of existing methodologies. Additionally, we have included comprehensive guidelines and practical examples for applying the proposed structural complexity metric in DW development scenarios to enhance its utility among practitioners. The proposed metric can be utilized to ensure the accuracy of the requirements model, thereby improving the overall quality of DW.

Automated building layout generation: Implementation and comparison of STREAMER Early Design Configurator and SDaC Layout Designer

When designing new buildings, various constraints must be taken into account. These constraints encompass factors such as the overall building size, the number, function, and size of required spaces, and even the surrounding neighborhood's existing buildings, infrastructure, and natural environment. Particularly the requirements of interior layout design are usually coupled with each other, making the design process more complex and time-consuming. This paper proposes a novel manner to consider the requirements, aiming to facilitate the generation process of building's interior layout during the early design phase. Besides, this paper aims to generate vectored output, which is more applicable to export BIM model, which also benefits the subsequent design and construct steps. This paper introduces STREAMER Early Design Configurator (EDC) and SDaC Layout Designer, both of which formats the user's requirements, semi-automatically generates interior layouts and exports IFC models of the floor plans. STREAMER EDC is used to discover the potential layouts of large-scale buildings with many rooms such as hospitals, while SDaC Layout Designer focuses on residential houses with comparatively fewer rooms. These drafts are enriched with additional data, including placement information, volume details, and space utilization. Ultimately, the floor plans are exported into three-dimensional objects and exported in the IFC format. This comprehensive step paves the way for more efficient and informed building design, promoting greater flexibility and adaptability in the early stages of the architectural process.

Geometry optimization in the schematic design phase of low-energy buildings for all European climates through genetic algorithms

The ongoing transition towards resilient and energy-efficient cities highlights the design of new buildings as a critical concern. With the increasing urbanization in Europe and the concurrent need for energy reduction and decarbonization, it is crucial to address energy efficiency in the construction sector, as by the recently adopted European Energy Performance of Building Directive. In this context, the article focuses on the role of geometry optimization in the schematic design phase of low-energy buildings, by considering it among different European climates. Indeed, this study aims to propose a multi-objective optimization approach (using a genetic algorithm) to optimize the “genes” of the building, e.g., shape, proportions, orientation and window-to-wall ratio of residential buildings in the early design phase, to cut off a significant portion of energy consumptions. The objective is thus to minimize heating and cooling energy needs based on the best combination of the above-mentioned genes. The research investigates optimal solutions across various European climates based on the Köppen-Geiger classification, comparing the results for 19 European cities with varying climate. The findings confirm that the energy needs of buildings are strongly influenced by the climate conditions of their location. The study, which comprehends almost 250.00 different configurations, assesses, as expected, the strong effect of climate conditions on the energy needs and highlights the cross-shaped buildings as the best performing in 17 over 19 sites investigated. The optimal solution for each site leads to total energy needs that are comprised between 4.58 kWh m-2 y-1 in Porto and 19.84 kWh m-2 y-1 in Goteborg, with an average across all cities of 12.45 kWh m-2 y-1. The research provides valuable insights for professional in the construction sector, aiding them in making informed decisions during the schematic design phase to achieve low-energy buildings that are adapted to specific climates.