Design Of Engineering Systems Research Articles

Optimizing functionally graded tibial components for total knee replacements: a finite element analysis and multi-objective optimization study

The optimal design of complex engineering systems requires tracing precise mathematical modeling of the system’s behavior as a function of a set of design variables to achieve the desired design. Despite the success of current tibial components of knee implants, the limited lifespan remains the main concern of these complex systems. The mismatch between the properties of engineered biomaterials and those of biological materials leads to inadequate bonding with bone and the stress-shielding effect. Exploiting a functionally graded material for the stem of the tibial component of knee implants is attractive because the properties can be designed to vary in a certain pattern, meeting the desired requirements at different regions of the knee joint system. Therefore, in this study, a Ti6Al4V/Hydroxyapatite functionally graded stem with a laminated structure underwent simulation-based multi-objective design optimization for a tibial component of the knee implant. Employing finite element analysis and response surface methodology, three material design variables (stem’s central diameter, gradient factor, and number of layers) were optimized for seven objective functions related to stress-shielding and micro-motion (including Maximum stress on the cancellous bone, maximum and mean stresses on predefined paths, the standard deviation of mean stress on paths, maximum and mean micro-motions at the bone-implant interface and the standard deviation of mean micro-motion). Then, the optimized functionally graded stem with 6 layers, a central diameter of 5.59 mm, and a gradient factor of 1.31, was compared with a Ti6Al4V stem for various responses. In stress analysis, the optimal stem demonstrated a 1.92% improvement in cancellous bone stress while it had no considerable influence on the maximum, mean, and standard deviation of stresses on paths. In micro-motion analysis, the maximum, mean, and standard deviation of mean micro-motion at the interface were enhanced by 24.31%, 39.53%, and 19.77%, respectively.

Discrepancies of Functional Requirements of Façade Opening System between Real-Life Public and Built Environment Experts Focusing on Thermal Comfort and Ventilation

A survey was conducted to analyze the discrepancies of the functional requirements of the façade system in residential units among 605 of the real-life public and 73 experts. Personal and housing information, resident life patterns, public façade usage behavior, and functional requirements were collected from the respondents. Both the public and experts recognized insulation as the main function of façade opening systems. More than 85% of the public and experts opened windows for ventilation, but ventilation was ranked 3rd amongst the public and 4th amongst experts in the main functions list of façade systems. The public cited the inflow of fine dust as the main reason for dissatisfaction with opening windows. In contrast, the experts cited a decrease in thermal comfort due to the inflow of external moisture as the reason for dissatisfaction with opening windows. The results showed that discrepancies exist between the public and experts’ perceptions of the main function of housing façade systems. Analyzing the common points and differences between the public and experts’ perception can help in developing façade system design and control technology.

Strategy to systematically design and deploy the ITER plasma control system: A system engineering and model-based design approach

The paper details the process of developing the ITER Plasma Control System (PCS), that is, how to design and deploy it systematically, in the most efficient and effective manner. The integrated nature of the ITER PCS, with its multitude of coupled control functions, and its long-term development, calls for a different approach than the design and short-term deployment of individual controllers. It requires, in the first place, a flexible implementation strategy and system architecture that allows system re-configuration and optimization throughout its development. Secondly, a model-based system engineering approach is carried out, for the complete PCS development, i.e. both its design and deployment. It requires clear definitions for both the PCS role and its functionality, as well as definitions of the design and deployment process itself. The design and deployment process is shown to allow tracing the relationships of the many individual design and deployment aspects, such as system requirements, assumed operation use-cases and response models, and eventually verification and functional validation of the system design. The functional validation will make use of a dedicated PCS simulation platform that includes the description of the control function design as well as plant, actuator and sensor models that enable the simulation of these functions. By establishing a clear understanding of the interconnected steps involved in designing, implementing, commissioning, and operating the system, a more systematic approach is achieved. This ensures the completion of a comprehensive design that can be deployed efficiently, hence preventing the loss of precious operational time needed to debug and retune control functions and more importantly avoiding tokamak discharge disruptions.

Justice-Embedded Requirements Engineering (JERE) for system design

AbstractWe have a unique opportunity to consider justice in our design of a cleaner energy system. This paper introduces the Justice-Embedded Requirements Engineering (JERE) process, which was created to enable engineers to consider project goals, requirements, and potential project impacts on historically marginalized, climate-vulnerable communities. Given JERE's focus on energy technologies, we demonstrate the process using a concentrating solar power example. JERE provides engineers with a tool to better ensure justice is embedded in the system design process from the beginning.

Multi-method examination of contact mechanics in orthotropic layers under gravity

Analyzing the behavior of intelligent unconventional materials under diverse contact scenarios, in comparison to conventional materials, is a critical step in the initial design of engineering systems. This paper presents the development of analytical and numerical approaches for analyzing contact mechanics in a system comprising an orthotropic layer resting on a rigid foundation. Parametric analyses include the consideration of cylindrical and flat punch profiles. Analytical formulation utilizes integral transform methods to convert planar elasticity equations into a Cauchy-type singular integral equation of the second kind. A detailed description of the solution technique for the integral equation is provided, encompassing both the analytical formulation and the required discretization for obtaining the solution. Subsequently, a finite element approach (FEA) is employed to approximate contact bodies using a collection of finite elements, while contact boundaries are approximated by utilizing a set of polygons. Alternatively, the problem is addressed using the Multilayer Perceptron approach, a form of artificial neural network frequently applied in diverse machine learning applications, including scenarios involving contact problems. Finally, the resolution to the problem is achieved by employing the multilayer perceptron (MLP) method. The study yields determinations of contact stresses under the punch, the critical separation load resulting in the detachment of the orthotropic layer from the rigid foundation, and the corresponding separation distance. This analysis considers a range of dimensionless parameters and explored the behavior of diverse orthotropic materials. Comparisons between the results of the analytical method and computations from FEA and MLP reveal the exceptional accuracy attained by all three approaches. As the pioneer study employing three distinct approaches to examine continuous contact mechanics within an orthotropic layer under the influence of gravity, this research constitutes a valuable point of reference for upcoming scholars in the field.

Virtual design on the heat pump refrigeration cycle: challenges and approaches

Virtual product design (VPD) of the refrigeration cycle has long been a major subject of many engineers and manufacturers. Issues should be the question of how simulation gets close to the real world in virtual ways. It is especially true when we consider variable refrigerant flow (VRF) heat pumps, having various types of indoor unit combinations, long and complicated piping configurations, and simultaneous cooling and heating functions under extreme weather conditions. In this regard, model-based systems engineering (MBSE) or model-based design (MBD) guides a methodology to make a virtual model easily. Now, there are plenty of engineering platforms and tools to make a strong simulation model, which provides interfacing technology among multiple physics, different dimensions, and codes in different languages. In the study, the authors share their experience in the virtual design for a VRF heat pump system including the architecture, multiple physics, and the connection with the control SW. Modelica, an acausal and object-oriented modeling language, constructs the backbone of the model to describe the dynamic behavior of refrigerants and to combine external components in the form of functional mock-up units (FMUs). The developed virtual model is validated against measured data, showing it can reproduce all the essential physics of interest both in qualitative and quantitative ways.

Complexity biomechanics: a case study of dragonfly wing design from constituting composite material to higher structural levels.

Presenting a novel framework for sustainable and regenerative design and development is a fundamental future need. Here we argue that a new framework, referred to as complexity biomechanics, which can be used for holistic analysis and understanding of natural mechanical systems, is key to fulfilling this need. We also present a roadmap for the design and development of intelligent and complex engineering materials, mechanisms, structures, systems, and processes capable of automatic adaptation and self-organization in response to ever-changing environments. We apply complexity biomechanics to elucidate how the different structural components of a complex biological system as dragonfly wings, from ultrastructure of the cuticle, the constituting bio-composite material of the wing, to higher structural levels, collaboratively contribute to the functionality of the entire wing system. This framework not only proposes a paradigm shift in understanding and drawing inspiration from natural systems but also holds potential applications in various domains, including materials science and engineering, biomechanics, biomimetics, bionics, and engineering biology.

Modeling of the process of multicriteria distribution and execution of work packages in the design of technological systems

Modeling of the process of multicriteria distribution and execution of work packages in the design of technological systems

From applied ethics and ethical principles to virtue and narrative in AI practices

AbstractThe question of how we can use ethics and ethical frameworks to avert the negative consequences of AI through guidance on human behaviour and the design of technological systems has recently been receiving increasing attention. The appropriate response to an ethics of AI has certainly been contentious. For some years the wisdom of deontology and utilitarianism in the ethics of technology has been questioned. Today, a kind of AI ethics principlism has gained a degree of widespread acceptance, yet it still invites harsh rejections in recent scholarship. In this paper, we wish to explore the contribution to an ethics of AI made by a narrative philosophy and ethics of technology inspired by the ‘little ethics’ of Paul Ricoeur, and virtue ethics of Alasdair MacIntyre, most recently and promisingly built upon by Wessel Reijers and Mark Coeckelbergh. The objective of this paper is to examine the extent to which a narrative and virtue based ethics (or, VPD, i.e., virtuous practice design) might be a plausible candidate for the foundation of an ethics of AI, or rather ethical AI practice. This will be achieved by exploring the ways in which this approach can respond to some of the significant faults with or critiques of applied and principles and guidelines based ethical approaches to AI ethics.

Robust Parameter Design on Dual Stochastic Response Models With Constrained Bayesian Optimization

In engineering system design, minimizing the variations of the quality measurements while guaranteeing their overall quality up to certain levels, namely the robust parameter design (RPD), is crucial. Recent works have dealt with the design of a system whose response-control variables relationship is a deterministic function with a complex shape and function evaluation is expensive. In this work, we propose a Bayesian optimization method for the RPD of stochastic functions. Dual stochastic response models are carefully designed for stochastic functions. The heterogeneous variance of the sample mean is addressed by the predictive mean of the log variance surrogate model in a two-step approach. We establish an acquisition function that favors exploration across the feasible and optimality-improvable regions to effectively and efficiently solve the stochastic constrained optimization problem. The performance of our proposed method is demonstrated by the extensive numerical and case studies. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Many manufacturing processes involve undesirable variations, which create variations in the final products. For example, many emerging manufacturing processes, such as nanomanufacturing, involve complex physical and chemical dynamics and transformation, creating variations in the manufacturing output. In such processes, it is crucial to design the manufacturing processes or products so that they have minimum variations in their quality. Meanwhile, it is also important to maintain the overall quality of the designed processes or products. Furthermore, acquiring data from many advanced manufacturing processes is often very costly, especially in the designing stage. In this work, we propose a data-driven method that automatically finds the best setting of manufacturing processes or products with the minimum variations of quality and a given constraint on the average quality satisfied. Our proposed method is used before conducting every experiment; It analyzes the historical data from previous experiments and provides a setting to be used in the next experiment. Our proposed method efficiently utilizes the historical data, and thus finds the best robust setting by conducting only a small number of experiments.

A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis

Integrating deep learning methodologies is pivotal in shaping the continuous evolution of computer-aided design (CAD) and computer-aided engineering (CAE) systems. This review explores the integration of deep learning in CAD and CAE, particularly focusing on generative models for simulating 3D vehicle wheels. It highlights the challenges of traditional CAD/CAE, such as manual design and simulation limitations, and proposes deep learning, especially generative models, as a solution. The study aims to automate and enhance 3D vehicle wheel design, improve CAE simulations, predict mechanical characteristics, and optimize performance metrics. It employs deep learning architectures like variational autoencoders (VAEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs) to learn from diverse 3D wheel designs and generate optimized solutions. The anticipated outcomes include more efficient design processes, improved simulation accuracy, and adaptable design solutions, facilitating the integration of deep learning models into existing CAD/CAE systems. This integration is expected to transform design and engineering practices by offering insights into the potential of these technologies.

Extending UWOC System Applications through Photon Transmission Dynamics Study in Harbor Waters

Underwater wireless optical communication (UWOC) in harbor waters can facilitate real-time monitoring underwater instruments for environmental monitoring, underwater inspection, and maintenance tasks. This study delves into the complex dynamics of UWOC in four distinct harbor waters. The research employs Monte Carlo method incorporated with Fournier–Forand scattering phase function for simulating photon transmission. Key parameters such as the Transmitted full divergence angle, received aperture, and Field of View (FOV) are meticulously evaluated for their impact on power loss and time delay spread. Notably, the normalized power loss and time delay spread are found to be more significantly affected by communication distance than water quality, and the traditional Beer–Lambert law is ineffective in harbor water. The power loss of Harbor II, III, and IV are found to be 14.00 dB, 31.59 dB, and 41.59 dB lower than that of Harbor I, and the time delay spread of Harbor II, III, and IV is 30.56%, 9.67%, and 0.49% times that of the Harbor I under certain conditions. In addition, increasing the received aperture and FOV, particularly over longer distance, make little contribution to reduce the power loss and mitigate the time delay spread. Based on the fixed transmitted full divergence angle, the most applicable received FOV range is 1–3.2 rad, and the most ideal received aperture is 0.15–0.4 m. Under these conditions, the variation in normalized power loss is less than 2 dB. Additionally, the time delay spread remains within the same order of magnitude with the attenuation length (AL) held constant. These conclusions hold substantial technical relevance for the engineering design of UWOC systems in harbor waters.

ABSS: An Adaptive Batch-Stream Scheduling Module for Dynamic Task Parallelism on Chiplet-based Multi-Chip Systems

Thanks to the recognition and promotion of chiplet-based High-Performance Computing (HPC) system design technology by semiconductor industry/market leaders, chiplet-based multi-chip systems have gradually become the mainstream. Unfortunately, programming such systems to achieve efficient computing is a challenge, especially when considering dynamic task parallelism. This paper presents an Adaptive Batch-Stream Scheduling (ABSS) module for dynamic task parallelism on chiplet-based multi-chip systems. To this end, we propose an adaptive batch-stream scheduling method based on Graph Convolution Network (GCN) classifier to select the appropriate scheduling scheme. We further design a chiplet-based core-cluster binding mechanism, which establishes the affinity between threads and core-clusters on CPU-compute die. Moreover, to achieve dynamic workload balance, we propose a chiplet-based nearest task stealing method. We implement our ABSS module on the HiSilicon Kunpeng-920 chiplet-based multi-chip system. Experiments show that it outperforms state-of-the-art parallelism solutions, such as Intel Threading Building Blocks.

Optimization design of a hybrid thermal runaway propagation mitigation system for power battery module using high-dimensional surrogate models

The demand for high energy density in lithium-ion battery packs for electric vehicles poses a challenge to maintaining its optimum operating temperature while reducing the risk of thermal runaway (TR) propagation. This study proposes a novel hybrid TR propagation mitigation system that balances heat transfer and thermal insulation requirements using low and high thermal conductivity phase change materials (PCM), heat pipes (HP), and air-cooling. The design and optimization of such a mitigation system are complex due to the many design parameters involved. The Adaptive-Kriging-High dimensional model representation (Adaptive-Kriging-HDMR) is used to establish a surrogate model of the system, and the sensitivity of the system's design parameters is evaluated with the maximum battery temperature and the system weight as the targets, thereby improving the efficiency of model calculation and reducing the dimension of optimization parameters. Then, the design of the sensitive parameters is optimized using an extended elitist non‐dominated sorting genetic algorithm (E-NSGA-II) multi-objective optimization algorithm. The results show that the modeling difficulty and optimization calculation time are significantly reduced by using a surrogate model. The calculation time for a single surrogate model only takes a few seconds instead of several hours for the original three-dimensional heat transfer and flow calculation. The thermal conductivity of high thermal conductivity-PCM, the distance between battery and low thermal conductivity-PCM, the battery spacing, and the HP length significantly affect the system. The optimized system substantially reduces the overall weight of the battery system while ensuring its good heat dissipation capability. In the case of TR in a single battery, the system succeeds in limiting the TR propagation to the same row, with the maximum battery temperature in the second row being only 64.3 °C, well below the battery TR trigger point. Under more severe conditions, such as TR occurring in two batteries simultaneously, the maximum battery temperature in the second row is 155.5 °C, and no TR spreads to the adjacent row. This study provides a rapid and effective method for designing a TR propagation mitigation system. It can serve as a reference for the engineering design and optimization of battery thermal management systems.

Water distribution system modelling of GIS-remote sensing and EPANET for the integrated efficient design

Abstract Urban settlement depends on water distribution networks for clean and safe drinking water. This research incorporates geographic information systems (GIS), remote sensing (RS), and hydraulic modelling software EPANET to analyse and construct water distribution systems in Bota town, India. Satellite images and hydrological data have been utilized for the management of the Bota town's water supply network, sources to cater the demand for urban centres. EPANET simulates hydraulic behaviour in the water distribution system under different operating situations. EPANET simulation shows network leaks, low pressure, and substantial head loss. These findings have advised for water distribution system improvements by analysing network shortcomings. Booster pumps, new pipelines, and repairing of existing leakages are examples of such improvements. GIS, RS, and EPANET provided a comprehensive water distribution system study and more accurate and efficient improvement identification. This study emphasizes the necessity of new technologies in water distribution system analysis and design. The study solves Bota town's water distribution system problems of low pressure, high head loss, and leaks utilizing GIS, RS, and EPANET. The findings of this research can help in enhancing the water delivery systems in other towns with comparable issues.

Fostering responsible innovation with critical design methods

ABSTRACT In light of increasingly harmful social, psychological, and environmental impacts stemming from the tech industry, this article contributes to ongoing conversations regarding the need for more rigorous ethical deliberation in the engineering design workflow. We present two examples of pedagogical interventions dedicated to injecting critical design methods into the education of future tech developers to help foster responsible innovation: 1) a cross-disciplinary curricular intervention with English and Systems Design Engineering students; 2) a series of Responsible Innovation workshops conducted with students. Critical design, an arts-based research practice that resists unreflective technological progress, is uniquely situated to enhance current approaches in engineering ethics curricula by creating space for reflection about and design-based responses to the impacts of tech innovation. We argue that methods and expertise from the arts and humanities – disciplines that excel in the critical contextualization of technological progress – can help foster an ethos of responsible innovation in engineering education.

Automata-based Software Engineering for Control System Design and Verification

The automata composition is defined as the basic language construct of automata programming. Incorporating automata composition into an arbitrary programming language allows the development of automata programs in that language. Methods for specification and verification of reactive systems are defined in detail. All kinds of correctness formulas for a reactive system with respect to its specification are defined. In addition, correctness formulas for verification using the full invariant of the reactive system are developed. The Event-B manual begins with a brilliant illustration of the basic Event-B methods using the example of a car traffic control problem on a narrow bridge. However, the latter refinement in this illustration generates a complex cumbersome program. A simpler and shorter solution to this problem was presented in our work [7] using automata programming approach. Our solution was not easy because 4 non-trivial bugs were found by verification in Event-B. This paper describes our third attempt to construct a short simple automata program to solve this problem. Verification of the automata program in Event-B and Why3 systems was carried out. No errors were found. For verification, a reactive system model is built on Why3, which is simpler and more universal than the why3-do model.

Adaptation Through Communication: Assessing Human–Artificial Intelligence Partnership for the Design of Complex Engineering Systems

Abstract Exploring the opportunities for incorporating Artificial Intelligence (AI) to support team problem-solving has been the focus of intensive ongoing research. However, while the incorporation of such AI tools into human team problem-solving can improve team performance, it is still unclear what modality of AI integration will lead to a genuine human–AI partnership capable of mimicking the dynamic adaptability of humans. This work unites human designers with AI Partners as fellow team members who can both reactively and proactively collaborate in real-time toward solving a complex and evolving engineering problem. Team performance and problem-solving behaviors are examined using the HyForm collaborative research platform, which uses an online collaborative design environment that simulates a complex interdisciplinary design problem. The problem constraints are unexpectedly changed midway through problem-solving to simulate the nature of dynamically evolving engineering problems. This work shows that after the unexpected design constraints change, or shock, is introduced, human–AI hybrid teams perform similarly to human teams, demonstrating the capability of AI Partners to adapt to unexpected events. Nonetheless, hybrid teams do struggle more with coordination and communication after the shock is introduced. Overall, this work demonstrates that these AI design partners can participate as active partners within human teams during a large, complex task, showing promise for future integration in practice.

Extension and Validation of the Turbomachinery Capabilities of SU2 Open Source Computational Fluid Dynamic Code

Abstract Computational Fluid Dynamic (CFD) tools have revolutionized the way to design engineering systems, but most established codes are proprietary and closed-source, making it difficult, if not impossible, to modify, debug, or add new features to the code. To provide a freely available open-source CFD code for turbomachinery aerodynamics and aeroelasticity, this paper enhances the turbomachinery capabilities of the open-source SU2 code and demonstrates its capabilities of single-passage steady simulation, full-annulus unsteady simulation, and aeroelasticity analysis in two high-speed compressors, namely NASA Stage 35 and TUDa-GLR-OpenStage, and a linear cascade SC1. For the single-passage steady simulation of NASA Stage 35, the SU2 results are validated against the measured data and verified against the commercial solver ansys cfx, and the performance characteristics results are in reasonably good agreement with each other. For the single-passage steady simulation of TUDa-GLR-OpenStage, grid and turbulence model sensitivity studies are performed and results are validated against the measured data, and SU2 can predict both the performance characteristics and the radial profiles with sufficient accuracy. For the full-annulus unsteady simulation of NASA Stage 35, it is demonstrated that SU2 can predict the propagation of inlet distortion equally well as ansys cfx. For the linear cascade, SU2 can predict the unsteady pressure and aerodynamic damping coefficient accurately. The presented results demonstrate the turbomachinery aerodynamics and aeroelasticity capabilities of SU2. The major modifications of SU2 made in this work will be shared with the code maintainer and the community in the future.

Enhanced thermal performance and entropy generation analysis in a novel cavity design with circular cylinder

AbstractAnalyzing fluid dynamics and heat transfer holds significant importance in the design and enhancement of engineering systems. The current investigation utilizes the finite element method to explore natural convection and heat transfer intricacies within a novel cavity containing an inner circular cylinder under steady and laminar flow conditions. The principal aim of this study is to assess the impact of Rayleigh number (Ra), Bejan number (Be), and the presence of adiabatic, hot, and cold cylinders on heat transfer, entropy generation, and fluid flow. The range of Ra considered in this investigation spans from 103 to 106, while the Prandtl number for the air is fixed at 0.71. The findings illustrate that the presence of a cylinder leads to higher Be as Ra increase, compared to scenarios where no cylinder is present. This observation suggests that buoyancy forces dominate in the absence of a cylinder, resulting in significantly enhanced convective heat transfer efficiency. However, the presence of a heated cylinder within the tooth‐shaped cavity exerts a substantial influence on the overall thermal performance of the system. Notably, the average Nusselt Number (Nu) experiences a remarkable increase of 41.97% under the influence of a heated cylinder, when compared to situations where a cold cylinder is present. This elevated average Nu signifies improved heat transfer characteristics, ultimately resulting in an overall improvement in the thermal system's efficiency.