Early Stages Of System Design Research Articles

Operational greenhouse gas emissions of various energy carriers for building heating

The decarbonisation of the building heating sector requires a shift from decentralised fossil fuel heating appliances to systems converting energy carriers with low greenhouse gas (GHG) emissions. However, for certain energy carriers, a considerable portion of GHG emissions arises upstream during production, processing and transportation, rather than during energy conversion. Accurately quantifying these indirect GHG emissions typically requires life cycle assessments, which are often resource-intensive and impractical during the early stages of energy system design. This study introduces operational GHG emissions as a pragmatic metric for the preliminary assessment of energy carrier environmental impact in building heating applications. These operational GHG emissions include both direct CO2 emissions and indirect CO2, CH4 and N2O emissions. Based on a comprehensive literature analysis, average estimates are proposed for the operational GHG emissions of various energy carriers within a European context, including natural gas, oil, coal and wood, as well as the average European and Belgian electricity grid, and hydrogen from various production methods. The findings underscore the significant contribution of indirect GHG emissions, as the selection of the energy carrier with the lowest environmental impact hinges on whether direct emissions alone or the broader operational GHG emissions are considered. By integrating operational GHG emissions into the early design stages of energy systems, stakeholders can make more informed decisions about which energy systems warrant further investigation, thereby facilitating more sustainable energy system development from the outset.

Analysis of AP1000 Small-Break Loss-of-Coolant Accident Using Reactor Transient Simulator

The Westinghouse Electric Company’s Advanced Passive Reactor (AP1000) is characterized by the incorporation of passive safety systems (PSSs) designed to ensure core cooling during transient events. The assessment of PSSs requires evaluation of their performance through a combination of experiments and simulations employing various thermal-hydraulic codes. In addition, detailed evaluation of PSSs for a specific reactor system transient analysis such as loss-of-coolant-accident analysis supports understanding representative integral effects test facility development and the further evolution model development and assessment process. Developing a reactor system code is a complex and time-consuming process that requires significant engineering expertise and effort. It can take several months to even years to complete in the early stages of reactor system design and analysis. However, this process can be expedited through the use of transient simulator models for similar reactor systems, which can be used for lesson learning and training purposes. This study uses the Personal Computer Transient Analyzer (PCTRAN) code. The main advantage of PCTRAN is its ease of use and ability to run faster than real time. This study presents the results obtained for a small-break loss-of-coolant accident (SBLOCA) for two breaks using the full version (licensed) of PCTRAN. The purpose of this investigation is to evaluate the overall system behavior during the postulated SBLOCA event as well as assess the capability of the PCTRAN code to reproduce the system response during transient events. The obtained results were compared with the Westinghouse NOTRUMP system code. The PCTRAN code proved to be reliable in predicting the qualitative behavior of the system in both transient cases. As for the system response, it was found that it is contingent on the activation time of the PSSs. The differences in reactor coolant system pressure between the two codes were attributed to the critical flow model and simplification of mass and energy balance. Despite PCTRAN’s limitations, it can still provide a reasonable prediction of various reactor parameters such as pressure, mass flow rate, and void fraction during a SBLOCA scenario. It is worth noting that PCTRAN currently employs a bulk approach similar to that of the Modular Accident Analysis Program (MAAP) and MELCOR codes. However, the upcoming version of PCTRAN will include an artificial intelligence–based detection and accident prevention system, as well as different models for different reactor components. Consequently, PCTRAN has the potential to be upgraded to match the system thermal-hydraulic codes of the U.S. Nuclear Regulatory Commission and become more widely used in cybersecurity to safeguard nuclear power plants from cyberattacks.

Development and Proof of Concept of a Predictive Model of Flight Deck Cognitive Workload

Abstract: Workload can be regarded as the “cost” (in information processing terms) of performing a given flight task. Predictive models of workload are valuable at the early stages of system design to identify potentially excessive demands on the human operator before major system development commences. This paper describes the early development of a task-based predictive workload method. It utilizes a scenario-based timeline analysis supplemented by characterizations of the cognitive nature of the pilot’s tasks using Wickens’ multiple resource theory, followed by the identification of likely task conflicts and estimates of the influence of the task environment. Workload predictions were made for a manual approach, landing and roll-out scenario in a modern airliner. Predictions were compared against corresponding Bedford scale workload estimates. Results show that the predictive workload technique produced comparable estimates of cognitive workload measured using the Bedford scale.

Ecodesign approach for complex systems – Electric Vehicle case study

The reduction of environmental impacts from the early stages of product and system design is a major challenge. Thus, traditional ecodesign methods are not appropriate because they reason by fixing the functional definition of products/systems and address the complexity of these new systems in a simplistic way, which leads to a marginal reduction of impacts. Moreover, these methods do not consider the attractiveness for users, leading to an optimization oriented towards functional reduction. In this paper, a comprehensive ecodesign approach is presented suggesting a paradigm shift, which involves a hybrid ecodesign approach based on constraint modeling of systems based on the constraint satisfaction problem formalism (CSP) with more traditional environmental assessment methods to decide which design choices to make. This approach was applied to generate and characterize product and life cycle architectures of a serial hybrid vehicle powertrain focusing on the use and end-of-life phase.

Caseworkers’ participation in procurement: Infrastructuring Child Welfare Services in Norway

Procurement is a widely adopted collaborative approach for acquiring new systems in the public sector. It exemplifies a situation in which the early stages of digital system design define the boundaries and constraints of a new system that must be specified in the tender document (i.e., a binding offer). Researchers and government officials have long recognized the benefit of end-user participation in system design. Given the central role of the pre-tender phases in procurement processes, however, there is a need to better understand what affects user participation in such early stages. In this paper, we research a procurement process in municipal Child Welfare Services in Norway. We focus on caseworkers' participation in procuring a future case management system. We build on the concept of participatory infrastructuring to characterize how the meaning of participation was shaped through three overarching participatory infrastructuring practices of decision-making within a rigid procurement process: (i) scaling up the project, (ii) negotiating participation in meetings with potential suppliers and in tender documents, and (iii) positioning caseworkers as subject experts. The analysis of these practices reveals that the definition of user needs in the tender documentation and the creation of knotworks define both the boundary conditions and the modalities of participation. We contribute to the conversation on participatory infrastructuring in Computer-Supported Cooperative Work by discussing how participatory infrastructuring provides a conceptual understanding of participation in the context of municipal systems procurement.

Identification and Assessment of Obsolescence in the Early Stages of System Design

Obsolescence is the fact that an entity (physical or logical) is becoming outdated or no longer possesses the required level of performance. The objectives of this article are twofold. First, it is intended to contribute to the understanding of obsolescence propagation. Secondly, two supporting approaches for the Identification and Assessment phases are proposed: the House of Obsolescence and the System Obsolescence Criticality Analysis. The former allows the mapping of obsolescence propagation via dependencies, whether imposed changes are desired or imposed, by external actors to the system architecture. Whereas, the objective of the latter is to assign an obsolescence criticality index to the identified risks in order to prioritize them for solution or mitigation determination during the analysis phase. The tools make extensive use of the modeled system knowledge through the application of Systems Engineering. The application of these approaches is presented through an illustrative study.

Design of wind and solar energy supply, to match energy demand

Renewable energy sources have an intermittent character that does not necessarily match energy demand. Such imbalances tend to increase system cost as they require mitigation measures and this is undesirable when available resources should be focused on increasing renewable energy supply. Matching supply and demand should therefore be inherent to early stages of system design, to avoid mismatch costs to the greatest extent possible and we need guidelines for that. This paper delivers such guidelines by exploring design of hybrid wind and solar energy and unusual large solar installation angles. The hybrid wind and solar energy supply and energy demand is studied with an analytical analysis of average monthly energy yields in The Netherlands, Spain and Britain, capacity factor statistics and a dynamic energy supply simulation. The analytical focus in this paper differs from that found in literature, where analyses entirely rely on simulations. Additionally, the seasonal energy yield profile of solar energy at large installation angles is studied with the web application PVGIS and an hourly simulation of the energy yield, based on the Perez model. In Europe, the energy yield of solar PV peaks during the summer months and the energy yield of wind turbines is highest during the winter months. As a consequence, three basic hybrid supply profiles, based on three different mix ratios of wind to solar PV, can be differentiated: a heating profile with high monthly energy yield during the winter months, a flat or baseload profile and a cooling profile with high monthly energy yield during the summer months. It is shown that the baseload profile in The Netherlands is achieved at a ratio of wind to solar energy yield and power of respectively E w / E s = 1.7 and P w / P s = 0.6 . The baseload ratio for Spain and Britain is comparable because of similar seasonal weather patterns, so that this baseload ratio is likely comparable for other European countries too. In addition to the seasonal benefits, the hybrid mix is also ideal for the short-term as wind and solar PV adds up to a total that has fewer energy supply flaws and peaks than with each energy source individually and it is shown that they are seldom (3%) both at rated power. This allows them to share one cable, allowing “cable pooling”, with curtailment to -for example-manage cable capacity. A dynamic simulation with the baseload mix supply and a flat demand reveals that a 100% and 75% yearly energy match cause a curtailment loss of respectively 6% and 1%. Curtailment losses of the baseload mix are thereby shown to be small. Tuning of the energy supply of solar panels separately is also possible. Compared to standard 40° slope in The Netherlands, facade panels have smaller yield during the summer months, but almost equal yield during the rest of the year, so that the total yield adds up to 72% of standard 40° slope panels. Additionally, an hourly energy yield simulation reveals that: façade (90°) and 60° slope panels with an inverter rated at respectively 50% and 65% Wp, produce 95% of the maximum energy yield at that slope. The flatter seasonal yield profile of “large slope panels” together with decreased peak power fits Dutch demand and grid capacity more effectively.

Thermodynamic analysis of working fluids: What is the highest performance of the sub- and trans-critical organic Rankine cycles?

The thermodynamic performance limits of power cycles are governed not only by the first and second law of thermodynamics, but also by the cycle configuration and working fluid properties. The organic Rankine cycle (ORC) is a promising technology for low-and-medium temperature heat utilization. This work further develops the thermodynamic performance limits analysis framework of sub- and trans-critical ORCs under realistic heat source conditions, and investigates the impact mechanism of key property parameters on system performance. Working fluid thermodynamic properties are characterized using a corresponding state model with 5 property parameters that are usually available in the early stages of system design. The property and system parameters are optimized simultaneously using a multi-objective genetic algorithm. The thermodynamic performance limits, and the optimal working fluid properties and system operation conditions are identified. The obtained limits represent the highest performance of ORC. The obtained optimal property and system parameters represent the desired working fluid and system operation characteristics. The impact mechanism of key property parameters on system performance is discussed in detail in a sensitivity analysis. It is found that the critical temperature is the most important property parameter, and that it changes the preference over efficiency and compactness objectives. The obtained thermodynamic performance limits, optimal parameter sets, and impact mechanism provide insights for working fluid selection in ORC research and implementation.

Dynamic Simulation-Based Surrogate Model for the Dimensioning of Building Energy Systems

In recent decades, building design and operation have been an important field of study, due to the significant share of buildings in global primary energy consumption and the time that most people spend indoors. As such, multiple studies focus on aspects of building energy consumption and occupant comfort optimization. The scientific community has discerned the importance of operation optimization through retrofitting actions for on-site building energy systems, achieved by the use of simulation techniques, surrogate modeling, as well as the guidance of existing building performance and indoor occupancy standards. However, more knowledge should be attained on the matter of whether this methodology can be extended towards the early stages of thermal system and/or building design. To this end, the present study provides a building thermal system design optimization methodology. A data set of minimum thermal system power, for a typical range of building characteristics, is generated, according to the criterion of occupant discomfort in degree hours. Respectively, a surrogate model, providing a configurable correlation of the above set of thermal system dimensioning solutions is developed, using regression model fitting techniques. Computational results indicate that such a model could provide both desirable calculative simplification and accuracy on par with existing respective thermal load calculation standards and simplified system dimensioning methods.

Model-based safety engineering for autonomous train map

As a part of the digital revolution of railway systems, an autonomous driving train will use a complete and precise map of railway infrastructure to conduct operational actions. Nevertheless, the full autonomy of trains depends on the safety decisions management capacity both on-board and track-side. These decisions must be refined into safety requirements in order to continuously check the consistency between the perceived infrastructure and safety related properties. However, traditionally, the integration of safety analysis requires the intervention of human agent skills. This may be error-prone and in interference with the embedded aspect of the train map. In this paper, we propose a model-based approach to match between safety concepts expressed as an ontology, a derived safety model and a safety-extended railway infrastructure map model for autonomous trains. This approach is validated by railway safety case studies for autonomous train map. The integration of this model-based safety solution from the early stages of the map system design improves the safety decisions management process.

Medical image quality metrics for foveated model observers.

Purpose: A recently proposed model observer mimics the foveated nature of the human visual system by processing the entire image with varying spatial detail, executing eye movements, and scrolling through slices. The model can predict how human search performance changes with signal type and modality (2D versus 3D), yet its implementation is computationally expensive and time-consuming. Here, we evaluate various image quality metrics using extensions of the classic index of detectability expression and assess foveated model observers for search tasks. Approach: We evaluated foveated extensions of a channelized Hotelling and nonprewhitening matched filter model with an eye filter. The proposed methods involve calculating a model index of detectability ( ) for each retinal eccentricity and combining these with a weighting function into a single detectability metric. We assessed different versions of the weighting function that varied in the required measurements of the human observers' search (no measurements, eye movement patterns, size of the image, and median search times). Results: We show that the index of detectability across eccentricities weighted using the eye movement patterns of observers best predicted human performance in 2D versus 3D search performance for a small microcalcification-like signal and a larger mass-like. The metric with a weighting function based on median search times was the second best predicting human results. Conclusions: The findings provide a set of model observer tools to evaluate image quality in the early stages of imaging system evaluation or design without implementing the more computationally complex foveated search model.

A system level approach to estimate maximum load steps that can be applied on a fuel cell powered marine DC system

This paper presents a system level approach to estimate the maximum load steps on a hydrogen fuel cell powered marine system. In the proposed approach, a model has been developed to predict the system distribution voltage drop due to sudden load changes applied on the system. The estimated voltage drop is used as a metric to determine if a system can sustain the applied load change. Such technique is beneficial for system engineers in the early stage of marine system design and dimensioning. Additionally, the model can be used by a power management system to coordinate starting and stopping of fuel cells for improved system performance. In this work, the proposed approach is validated using a real-time hardware-in-loop simulation platform and it is demonstrated that the proposed approach is accurate within 1.2%.

A Simple Power Source Modeling and Experimental Investigation of a Spacecraft for EMC Applications

Simplified modeling of a spacecraft power system is proposed to predict the conducted electromagnetic compatibility (EMC) environment. A key element of the spacecraft conducted environment is the electrical power subsystem, which corresponds to the power source. Since the impedance of the power source has a significant effect on conducted interference, the assessment of power bus impedance is essential to control and manage spacecraft EMC. In the proposed model, all components inside the power system were modeled as simple equivalent circuits with lumped elements. The advantage is that it simplifies the complex power system so that its impedance can be quickly obtained at the early stage of spacecraft system design and development. The model is validated by comparing the calculated results with experimental data. The comparison results between simulation and experiment showed good agreements.

Prognostic systems representation in a function-based Bayesian model during engineering design

Prognostics and Health Management (PHM) systems are usually only considered and set up in the late stage of design or even during the system’s lifetime, after the major design decision have been made. However, considering the PHM system’s impact on the system failure probabilities can benefit the system design early on and subsequently reduce costs. The identification of failure paths in the early phases of engineering design can guide the designer toward a safer, more reliable and cost-efficient design. Several functional failure modeling methods have been developed recently. One of their advantages is to allow for risk assessment in the early stages of the design. Risk and reliability functional failure analysis methods currently developed do not explicitly model the PHM equipment used to identify and prevent potential system failures. This paper proposes a framework to optimize prognostic systems selection and positioning during the early stages of a complex system design. A Bayesian network, incorporating the PHM systems, is used to analyze the functional model and failure propagation. The algorithm developed within the proposed framework returns the optimized placement of PHM hardware in the complex system, allowing the designer to evaluate the need for system improvement. A design tool was developed to automatically apply the proposed method. A generic pressurized water nuclear reactor primary coolant loop system is used to present a case study illustrating the proposed framework. The results obtained for this particular case study demonstrate the promise of the method introduced in this paper. The case study notably exhibits how the proposed framework can be used to support engineering design teams in making better informed decisions early in the design phase.

Unified modeling for digital twin of a knowledge-based system design

• Integrating digital twin (DT) with model-based systems engineering (MBSE) for the system design complexity analysis and prediction. • A system design digital twin in 5 dimensions (SDDT 5D ) framework is introduced, which is able to map the actual system design process into the corresponding virtual space . • The core concepts and relationships of SDDT 5D are customized by a SysML profile that keeps every view of the system digital model unified. • The system model reduces ambiguity through unifying system requirements, functions, and structures and behaviors. While Model-Based Systems Engineering (MBSE) improves the ambiguity problem of the conventional document-based way, it brings management complexity. Faced with the complexity, one of the core issues that companies care about is how to effectively evaluate, predict, and manage it in the early system design stage. The inaccuracy of contemporary complexity measurement approaches still exits due to the inconsistency between the actual design process in physical space and the theoretical simulation in virtual space. Digital Twin (DT) provides a promising way to alleviate the problem by bridging the physical space and virtual space. Aiming to integrate DT with MBSE for the system design complexity analysis and prediction, based on previous work, an integration framework named System Design Digital Twin in 5 Dimensions was introduced from a knowledge perspective. The framework provides services for design complexity measurement, effort estimation, and change propagation prediction. Then, to represent the system design digital twin in a unified way, a modeling profile is constructed through SysML stereotypes. The modeling profile includes System design digital model in virtual space profile, system services profile, relationships profile and digital twin data profile. Finally, the system design of a cube-satellite space mission demonstrates the proposed unfiled modeling approach.

Security Risk Analysis Approach for Safety-Critical Systems of Connected Vehicles

Modern vehicles are no longer merely mechanical systems but are monitored and controlled by various electronic systems. Safety-critical systems of connected vehicles become vulnerable to cyberattacks because of increasing interconnection. At present, the security risk analysis of connected vehicles is mainly based on qualitative methods, while these methods are usually subjective and lack consideration for functional safety. In order to solve this problem, we propose in this paper a security risk analysis framework for connected vehicles based on formal methods. Firstly, we introduce the electronic and electrical architecture of the connected vehicle and analyze the attack surfaces of the in-vehicle safety-critical systems from three levels of sensors, in-vehicle networks, and controllers. Secondly, we propose a method to model the target of evaluation (i.e., in-vehicle safety-critical system) as a Markov decision process and use probabilistic computation tree logic to formally describe its security properties. Then, a probabilistic model checker PRISM is used to analyze the security risk of target systems quantitatively according to security properties. Finally, we apply the proposed approach to analyze and compare the security risks of the collision warning system under a distributed and centralized electrical and electronic architecture. In addition, from a practical point of view, we propose a Markov model generation method based on a SysML activity diagram, which can simplify our modeling process. The evaluation results show that we can have a quantitative understanding of the security risks at the system level in the early stage of system design.

Assaying the importance of system complexity for the systems engineering community

AbstractHow should organizations approach the evaluation of system complexity at the early stages of system design in order to inform decision making? Since system complexity can be understood and approached in several different ways, such evaluation is challenging. In this study, we define the term “system complexity factors” to refer to a range of different aspects of system complexity that may contribute differentially to systems engineering outcomes. Views on the absolute and relative importance of these factors for early–life cycle system evaluation are collected and analyzed using a qualitative questionnaire of International Council on Systems Engineers (INCOSE) members (n = 55). We identified and described the following trends in the data: there is little between‐participant agreement on the relative importance of system complexity factors, even for participants with a shared background and role; participants tend to be internally consistent in their ratings of the relative importance of system complexity factors. Given the lack of alignment on the relative importance of system complexity factors, we argue that successful evaluation of system complexity can be better ensured by explicit determination and discussion of the (possibly implicit) perspective(s) on system complexity that are being taken.

Thermodynamic performance limits of the organic Rankine cycle: Working fluid parameterization based on corresponding states modeling

The organic Rankine cycle (ORC) is a promising technology in low-and-medium temperature energy utilization. As power cycles operate between hot and cold heat sources, the universal behavior of working fluids presents upper limits in addition to the first and second laws on the system thermodynamic performance. Knowing these limits provides insights on what the highest possible performance of a given technology is, what the optimal working fluids are, and how the behavior of working fluids affects the system thermodynamic performance. In the present work, based on the principle of corresponding states, working fluid thermodynamic properties are captured and parameterized using 5 property parameters, which are usually available from chemical manufacturers and theoretical estimations, and thus are available in the early stages of system design. Hypothetical working fluids are defined in a continuous thermodynamic space that comprises these parameters. Multi-objective optimizations are performed in the thermodynamic space for the property parameters rather than ORC operation parameters. Optimal hypothetical working fluids are targeted under various given operation conditions and assumptions, representing the thermodynamic performance limits, beyond which further improvements are forbidden by the universal behavior of thermodynamic properties. Impacts of each property parameter on the ORC thermodynamic performance are analyzed. Furthermore, comparisons with the Carnot limit and current working fluids are presented. This work presents a methodology to define the thermodynamic performance limits, to target the desired characteristics in working fluids, and to reveal the mechanism of how system thermodynamic performance changes with certain working fluid property parameters.

Service modeling for opportunistic edge computing systems with feature engineering

The complex and opportunistic environment in which edge computing systems operate, poses a fundamental challenge for online edge system orchestration, resource provisioning and real-time responsiveness in response to user movement. Such a challenge needs to addressed throughout the edge system lifecycle, starting from the software development methodologies. In this paper, we propose a novel development process for modeling opportunistic edge computing services, which rely on (i) ETSI MEC reference architecture and Opportunistic Internet of Things Service modeling for the early stage of system analysis and design, i.e. domain model and service metamodel; and on (ii) feature engineering for evaluating those opportunistic aspects with data analysis. To address the identified opportunistic properties, at the service design phase we construct (both automatically and through domain expertise) Opportunistic Feature Vectors for Edge, containing the numerical representations of those properties. Such vectors enable further data analysis and machine learning techniques in the development of distributed, effective and efficient edge computing systems. Lastly, we exemplify the integrated process with a microservice-based user mobility management service, based on a real-world data set, for online analysis in MEC systems.

IoT-HarPSecA: A Framework and Roadmap for Secure Design and Development of Devices and Applications in the IoT Space

The past couple of years have marked continued growth in the applications and services of the Internet of Things (IoT). This has attracted the attention of new operators as well as institutional, corporate, and private investors in every sector of the economy, and as a result, new businesses are springing up rapidly. These include many start-up companies that are producing various kinds of useful IoT devices and Smart Applications (smart apps). While this can be seen as a boost for innovation in the IoT, some of these companies produce IoT devices and smart apps with security vulnerabilities. In this paper, we propose the IoT Hardware Platform Security Advisor (IoT-HarPSecA), a security framework intended to provide support to such IoT producers. IoT-HarPSecA offers three functionality features, namely security requirement elicitation, security best practice guidelines for secure development, and above all, a feature that recommends specific LightWeight Cryptographic Algorithms (LWCAs) for both software and hardware implementations. Accordingly, IoT-HarPSecA is composed of three main components, namely Security Requirements Elicitation (SRE) component, Security Best Practice Guidelines (SBPG) component, and LightWeight Cryptographic Algorithms Recommendation (LWCAR) component, each of them servicing one of the aforementioned features. We implement a command-line tool in C++ to serve as an interface between users and the proposed framework. IoT-HarPSecA can be employed during the early stages of IoT systems design, and it can also be used to facilitate the implementation of security in existing IoT systems. This paper presents a detailed description, design, and implementation of the SRE, SBPG, and LWCAR components of the proposed framework. Using real-world practical scenarios, we show how IoT-HarPSecA can be used to elicit security requirements and recommend appropriate LWCAs based on user inputs. While a full performance evaluation of the SRE and SBPG components is beyond the scope of this paper, we present a detailed performance evaluation of the LWCAR component, which shows that IoT-HarPSecA can serve as a roadmap for secure IoT development.