Design Of Complex Engineering Systems Research Articles

Multi-fidelity design framework integrating compositional kernels to facilitate early-stage design exploration of complex systems

Abstract Early-stage design of complex systems is considered by many to be one of the most critical design phases because that is where many of the major decisions are made. The design process typically starts with low-fidelity tools, such as simplified models and reference data, but these prove insufficient for novel designs, necessitating the introduction of high-fidelity tools. This challenge can be tackled through the incorporation of multi-fidelity models. The application of MF models in the context of design optimization problems represents a developing area of research. This study proposes incorporating compositional kernels into the autoregressive scheme (AR1) of Multi-Fidelity Gaussian Processes, aiming to enhance the predictive accuracy and reduce uncertainty in design space estimation. The effectiveness of this method is assessed by applying it to 5 benchmark problems and a simplified design scenario of a cantilever beam. The results demonstrate significant improvement in the prediction accuracy and a reduction in the prediction uncertainty. Additionally, the paper offers a critical reflection on scaling up the method and its applicability in early-stage design of complex engineering systems, providing valuable insights into its practical implementation and potential benefits.

AN INTEGRATED APPROACH TO DESIGNING ROBUST GAS-BEARING SUPPORTED TURBOCOMPRESSORS THROUGH SURROGATE MODELING AND CONSTRAINED ALL-AT-ONCE MULTI-OBJECTIVE OPTIMIZATION

Abstract This research introduces an innovative framework to engineering design to tackle the challenges of robustness against manufacturing deviations and holistic optimization simultaneously in a multi-disciplinary, multi-subsystems context. The methodology is based on an application of ensemble artificial neural networks, which significantly accelerates computational processes. Coupled with the Non-dominated Sorting Genetic Algorithm III, this approach facilitates efficient multi-objective optimization, yielding a comprehensive Pareto front and high-quality design solutions. Here, the framework is applied to the design of gas-bearing-supported turbocompressors. These systems are challenging due to their sensitivity to manufacturing variations, particularly in the gas-bearing geometry, which can lead to rotordynamic instability. Additionally, the interdependencies between the subsystems, such as axial and journal bearings, rotor, compressor impellers, and magnets, necessitate a multidisciplinary approach that spans aerodynamics, structural dynamics, rotordynamics, mechanics, loss analyses, and more. A clear tradeoff between system efficiency, mass-flow range, and robustness has been identified for the compressor design. Higher nominal compressor mass-flows, i.e. increased nominal power, is suggested to decrease the hypervolume of feasible manufacturing deviations. Hence, there is a sweet power spot for gas-bearing supported turbomachinery. Further, the framework's computational efficiency is on par with that of a university cluster, while only employing a desktop computer equipped with a consumer-grade graphics card. This work demonstrates a significant advancement in the design of complex engineering systems and sets a new standard for speed and efficiency in computational engineering design.

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.

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.

Applications of Nonlinear Analysis in Engineering Design

Nonlinear analysis plays a pivotal role in modern engineering design by addressing complex behaviors that linear models cannot capture. This article explores the mathematical foundations, methodologies, and real-world applications of nonlinear analysis, emphasizing its crucial role in optimizing and enhancing the design of complex engineering systems.

Goal Orientation in Music Composition and Other Social Behaviors Leading to the Common Quantitative Law

The power-law distribution is ubiquitous and common in many natural, engineering, and social systems. In this article, we found that goal orientation is the driving reason on the power-law distribution in typical social behaviors. A unified model is constructed to formulate the goal orientation mechanism in many social systems. Three mathematical characteristics of tonal music melody are found based on music composition theory. The constrained entropy maximization model (CEMM) is constructed, and the power-law distribution of melodic intervals is the result of solving the problem formulated in this model. Three other typical power-law models in economics, linguistics/informatics, and design of complex engineering systems are reviewed, and their goal orientation behaviors are analyzed. It is found that the models quantifying different social behaviors with goal orientation are consistent with the unified model. The power law is actually the result of the well-known social distribution mechanisms with limited resources, such as the maximal wealth with limited economic capability, the minimal cost under constrained resources, and the maximal entropy with restrictions. If without limit or constraint, these social systems would not observe the power law. This finding could inspire the study on other common laws in computational social systems.

A set‐based approach for hierarchical optimization problem using Bayesian active learning

AbstractIn the development of complex engineering systems, many engineers from different disciplines collaborate to identify feasible designs that satisfy all system requirements. Analytical target cascading (ATC) is a method for the design optimization of hierarchical, multilevel systems and has been successfully employed in the design of complex engineering systems. In this paper, we propose a novel data‐driven set‐based ATC (SBATC) method for hierarchical design optimization problems using machine learning techniques. The proposed SBATC offers two advantages in engineering processes. First, it decomposes hierarchical design optimization problems into sets of suboptimization problems. The feasible regions of the suboptimization problems are explored and cascaded to lower‐level optimization problems. Using the set‐based approach, couplings between two adjacent levels in the optimization process are not required. Second, an efficient strategy is employed to determine feasible regions based on Bayesian active learning (BAL). In BAL, the Gaussian process (GP) of all cost functions is trained. An acquisition function that combines the probability of feasibility and entropy search is evaluated using posterior distributions of the trained GP. The acquisition function is maximized to generate new sampling points around the feasible regions by balancing the exploitation and exploration of the design space. To verify the effectiveness of the proposed method, numerical examples of hierarchical optimization problems are evaluated.

Designer Selection for Complex Engineering System Design Projects Considering the Disciplines Demanded

Currently, technological advances and developments are leading to the rapid emergence of high-end technology products, accompanied by a variety of customer requirements, which will lead to engineering system design projects becoming more complex. Therefore, complex engineering design projects require designers with multidisciplinary backgrounds who can work together and approach complex problems. However, when company supervisors assign designers with interdisciplinary knowledge to complex projects, the knowledge gap of each discipline between designers will cause communication conflicts, which directly affects the efficiency of team communication and complex project execution. Therefore, it is very important for the design company to select a portfolio of designers with efficient interdisciplinary background communication. Thus, the main objective of our research is to propose a method to support company supervisors and their approach to the design team selection problem for complex engineering system design projects with consideration of the project’s required basic technical competence, personal competence, and multidisciplinary knowledge communication between the designers. The proposed method can help design firms to select the best multidisciplinary collaborative design team for complex engineering system design projects.

Natural Language Processing for systems engineering: Automatic generation of Systems Modelling Language diagrams

The design of complex engineering systems is an often long and articulated process that highly relies on engineers’ expertise and professional judgment. As such, the typical pitfalls of activities involving the human factor often manifest themselves in terms of lack of completeness or exhaustiveness of the analysis, inconsistencies across design choices or documentation, as well as an implicit degree of subjectivity. An approach is proposed to assist systems engineers in the automatic generation of systems diagrams from unstructured natural language text. Natural Language Processing (NLP) techniques are used to extract entities and their relationships from textual resources (e.g., specifications, manuals, technical reports, maintenance reports) available within an organisation, and convert them into Systems Modelling Language (SysML) diagrams, with particular focus on structure and requirement diagrams. The intention is to provide the users with a more standardised, comprehensive and automated starting point onto which subsequently refine and adapt the diagrams according to their needs. The proposed approach is flexible and open-domain. It consists of six steps which leverage open-access tools, and it leads to an automatic generation of SysML diagrams without intermediate modelling requirement, but through the specification of a set of parameters by the user. The applicability and benefits of the proposed approach are shown through six case studies having different textual sources as inputs, and benchmarked against manually defined diagram elements.

Visual Management (VM) supporting collaborative practices in infrastructure engineering design

Managing the design of complex engineering systems requires an organisational structure and an information system to support collaboration among all stakeholders. Technological developments in information management have the potential to facilitate interactions across physical boundaries, even more during the Covid-19 pandemic. Visual Management (VM) is an information management strategy, as well as a means for communication between individuals, supporting collaborative work. However, there is a lack of effective understanding of how digital VM can support infrastructure engineering design. The adoption of digital collaborative VM in the context addressed is new, under rapid evolution, and there is limited understanding of how the users embrace VM while interacting with it. The aim of the paper is to explore the adoption of VM, focusing on digital whiteboards, to support collaborative practices in design processes. The ongoing investigation is carried out in collaboration with an infrastructure design and consultancy company, and follows the action research approach. The VM effectiveness was investigated by analysing the whiteboards applicability to diverse functions and comparing digital and manual implementations. Initial findings include understanding digital whiteboards as a means for collaboration among individuals with different perceptions to establish a common point of view, as it allows the information to be transferred across time and space, identifies abnormalities, and supports problem-solving. By creating a common ground, it has the potential to support complex and emergent interactions in the collaborative space.

Robust Sampling Budget Allocation Under Deep Uncertainty

A novel methodology is introduced for optimally allocating a sampling budget. Sampling budget allocation problems arise frequently in various settings. For example, in the design of complex engineering systems, given both the complexity of these systems and the imperfect information on new technologies, designers often face deep uncertainty as to system performance. Consequently, designers need to sample multiple alternative designs under a limited budget. This article proposes a minimax regret approach to allocate the sampling budget in the presence of deep uncertainty pertaining to system performance. The objective is to maximize the probability of selecting the design with the minimum–maximum regret under a limited sampling budget and imperfect information. To effectively solve the minimax regret problem, an approximation methodology that provides good solutions with quantifiable uncertainty is developed. The essence of the methodology, which has the added benefit of being generally applicable to any multilevel optimization, is that all but the first level of multilevel optimization can be eliminated via a response surface. By sampling many values of a higher level decision’s variables, solving the next lower level optimization given those samples values, and calibrating a response surface to the objective function value eliminate one required optimization. Doing this repeatedly reduces the complexity of the multilevel optimization to a standard optimization. Regardless of the number of levels in the optimization, repeating this process ultimately leaves one with a single optimization whose objective function can be directly computed, given the highest level variables. Numerical experiments with two sampling allocation examples demonstrate both the benefit of the robust sampling budget allocation versus nonrobust formulations and the effectiveness of the proposed solution approach.

RBMDO Using Gaussian Mixture Model-Based Second-Order Mean-Value Saddlepoint Approximation

Actual engineering systems will be inevitably affected by uncertain factors. Thus, the Reliability-Based Multidisciplinary Design Optimization (RBMDO) has become a hotspot for recent research and application in complex engineering system design. The Second-Order/First-Order Mean-Value Saddlepoint Approximate (SOMVSA/FOMVSA) are two popular reliability analysis strategies that are widely used in RBMDO. However, the SOMVSA method can only be used efficiently when the distribution of input variables is Gaussian distribution, which significantly limits its application. In this study, the Gaussian Mixture Model-based Second-Order Mean-Value Saddlepoint Approximation (GMM-SOMVSA) is introduced to tackle above problem. It is integrated with the Collaborative Optimization (CO) method to solve RBMDO problems. Furthermore, the formula and procedure of RBMDO using GMM-SOMVSA-Based CO(GMM-SOMVSA-CO) are proposed. Finally, an engineering example is given to show the application of the GMM-SOMVSA-CO method.

Certifiable Risk-Based Engineering Design Optimization

Reliable, risk-averse design of complex engineering systems with optimized performance requires dealing with uncertainties. A conventional approach is to add safety margins to a design that was obtained from deterministic optimization. Safer engineering designs require appropriate cost and constraint function definitions that capture the risk associated with unwanted system behavior in the presence of uncertainties. The paper proposes two notions of certifiability. The first is based on accounting for the magnitude of failure to ensure data-informed conservativeness. The second is the ability to provide optimization convergence guarantees by preserving convexity. Satisfying these notions leads to certifiable risk-based design optimization (CRIBDO). In the context of CRIBDO, risk measures based on superquantile (aka conditional value-at-risk) and buffered probability of failure are analyzed. CRIBDO is contrasted with reliability-based design optimization (RBDO), in which uncertainties are accounted for via the probability of failure through a structural and a thermal design problem. A reformulation of the short column structural design problem leading to a convex CRIBDO problem is presented. The CRIBDO formulations capture more information about the problem to assign the appropriate conservativeness, exhibit superior optimization convergence by preserving properties of underlying functions, and alleviate the adverse effects of choosing hard failure thresholds required in RBDO.

A Reliability based Multidisciplinary Design Optimization Method with Multi-Source Uncertainties

The complexity of engineering systems is increasing greatly, and more coupled disciplines and multi-source uncertainties are involved in the design and development of complex engineered systems (CESs). Actually, uncertainties are ubiquitous in design of CESs, which can be mainly classified into alteatory uncertainty (AU) and epistemic uncertainty (EU). To gain high reliability and safety of CESs, the Reliability-based MDO (RBMDO) which considers uncertainties of design variables and parameters has become a hot research topic. In this paper, the quantification of aleatory and epistemic uncertainties based on the probability theory and convex set-theory, multidisciplinary comprehensive reliability evaluation index, adaptive collaborative optimization strategy based on the intelligent optimization algorithm, sequential multidisciplinary reliability analysis method based on the concurrent subspace optimization (CSSO) and performance measure approach (PMA), and hierarchical and hybrid sequential optimization and reliability assessment (HSORA) RBMDO strategy under multi-source uncertainties have been researched. All of the researches can expand and improve the RBMDO theoretical system, and also provide the effective method for the design and optimization of CESs under multi-source uncertainties.

Sensitivity-based adaptive sequential sampling for metamodel uncertainty reduction in multilevel systems

Decomposition-based technique is often used in the analysis and design of complex engineering systems for reducing the computational complexity by studying the subsystems decomposed from multilevel systems. Metamodels, as a replacement of original simulation models, can further alleviate the computational burden. However, discrepancy between the simulation models and metamodels, which is defined as metamodel uncertainty, may be introduced in the analysis process of multilevel systems owing to the lack of data. The metamodel uncertainties of sub-models will be further amplified because of the hierarchical uncertainty propagation and interaction between uncertainties, which will have a great impact on the system results. An adaptive sequential sampling strategy based on sensitivity is proposed in this paper so as to improve the prediction accuracy of system response. In this strategy, polynomial-chaos expansion is used to realize the forward propagation of metamodel uncertainty quantified by the Kriging model. The forward propagation is combined with optimization based on maximum variance criterion for searching the input locations that results in the largest variance of system response. Then, the indices of subsystems are obtained to make decisions about which subsystem needs extra samples by combining Karhunen-Loeve expansion and sensitivity analysis. The effectiveness of the proposed sequential sampling strategy method is verified by two mathematical examples and a multiscale composite material.

On the use of coordination strategies in complex engineered system design projects

AbstractCoordination of distributed design work is an important activity in large-scale and complex engineered systems (LSCES) design projects. Coordination strategies have been studied formally in system design optimization and organizational science. This article reports on a study to identify what strategies are used in coordination practice. While the literature primarily offers prescriptive coordination strategies, this study focussed on the contribution of individuals’ behaviours to system-level coordination. Thus, a coordination strategy is seen as a particular set of individual actions and behaviours. We interviewed professionals with expertise in systems engineering, project management and technical leadership at two large aerospace design organizations. Through qualitative thematic analysis, we identified two strategies used to facilitate coordination. The first we callauthority-basedand is enabled by technical know-how and the use of organizational authority; the second we callempathetic leadershipand includes interpersonal skills, leadership traits and empathy. These strategies emerged as complementary and, together, enabled individuals to coordinate complex design tasks. We found that skills identified in competency models enable these coordination strategies, which in turn support management of interdependent work in the organization. Studying the role of individuals contributes an expanded view on how coordination facilitates LSCES design practice.

A modelling framework to support design of complex engineering systems in early design stages

Production, assembly or logistic systems exist in widespread domains. It is agreed that more than 50% of life-cycle performance, costs and environmental impacts of such systems are due to those decisions that are made in their early design stages (Reich, Res Eng Design 28(4):411–419, https://doi.org/10.1007/s00163-017-0270-7, 2017). However, the large scale and multi-disciplinary essence of such systems make their design considerably challenging. Most of the design approaches follow a sequential approach such that the design in each lower level is finalized/frozen before proceeding to the next level. However, such approaches do not properly address the interaction between different design disciplines which may later lead to design inconsistencies. Therefore, this paper aimed to propose a modelling framework that allows having an integrated approach in the early design stages of such systems. To this end, first the framework prescribed developing an executable meta-architecture that can embody all the design requirements. Second, the framework describes the interconnections between the meta-architecture with certain supporting algorithms and optimization models. This allows generating and simulating different design alternatives and observing the impact of different design decisions on system integrated performance. Therefore, the proposed framework with its providing outcomes can be used to support the decision making in early design stages of such systems. The framework is applied in a real case study from the warehousing domain, which serves to show the practical application of the proposed framework.

Systems Modeling Language viewpoint utilization to facilitate shared mental models among system stakeholders

AbstractMental models are a vital part of complex engineered system design and operations and contribute to either success or failure in both system development and operations. This paper examines the Systems Modeling Language (SysML) viewpoints and views to determine whether they can illustrate elements of mental models and serve to increase communication and foster a common system understanding between individual stakeholders holding different perspectives. Two models were created in SysML, the first comparing SysML with established conceptual graphs of mental models and the second was the Lynx X‐ray Observatory, a project in concept development at National Aeronautics and Space Administration. This research revealed that shared SysML viewpoints and views can serve as conceptual graphs of shared mental models. It was also found that illustrating Lynx viewpoints and views was a valuable aid in eliciting concerns and desires of stakeholders, leading to a project more in line with desires of the stakeholders.

Surrogate-based aerodynamic optimisation of compact nacelle aero-engines

Genetic algorithms are a powerful optimisation technique for the design of complex engineering systems. Although computing power continuously grows, methods purely based on expensive numerical simulations are still challenging for the optimisation of aerodynamic components at an early stage of the design process. For this reason, response surface models are typically employed as a driver of the genetic algorithm. This reduces considerably the total overhead computational cost but at the expense of an inherent prediction uncertainty. Aero-engine nacelle design is a complex multi-objective optimisation problem due to the nonlinearity of transonic flow aerodynamics. This research develops a new framework, that combines surrogate modelling and numerical simulations, for the multi-objective optimisation of aero-engine nacelles. The method initially employs numerical simulations to guide the genetic algorithm through generations and uses a combination of higher fidelity results along with evolving surrogate models to identify a set of optimum designs. This new approach has been applied to the multi-objective optimisation of civil aero-engines which are representative of future turbofan configurations. Compared to the conventional CFD in-the-loop optimisation method, the proposed algorithm successfully identified the same set of optimum nacelle designs at a 25% reduction in the computational cost. Within the context of preliminary design, the method meets the typical 5% acceptability criterion with a 65% reduction in computational cost.

Evaluation of Three Design approach of a bipropellant propulsion system including multidisciplinary design optimization, Robust and Optimum-Robust

Considering the importance of the presence of uncertainties in the design of complex engineering systems, in this research multidisciplinary design optimization process for a bipropellant propulsion system in the presence of uncertainties, which in addition to minimizing the system mass, has a high robust. Based on this, the multidisciplinary design view of the bipropellant propulsion system is expressed in both optimum design and optimum robust design. The continued with the application of uncertainties, the mass, operational and geometric results of the propulsion system are expressed in terms of optimum design, robust design and optimum robust design. According to the results, it is shown that the lowest mass occurs in optimum design mode. But with uncertainties, it is observed at this point that it has the least robust and reliability. It also attempts to explain the difference between the concepts of robust design and optimum design with the help of results