Design Of Complex Engineering Systems Research Articles

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

What has engineering design to say about healthcare improvement?

This paper builds on the author’s keynote address to the Design Society’s 21st International Conference on Engineering Design in 2017 and in doing so provides a personal perspective to the question of the title. It begins by describing the engineering experience of the author which led to an understanding of the importance of taking a systems approach to the development of engineering products and services. This is followed by reflections on the development of a research portfolio focused on the design of complex engineering systems, inclusive design and healthcare improvement. The paper then reports on the recent work of engineers, clinicians and healthcare leaders, who came together under the guidance of the author, to explore how an engineering systems approach could be described that might simultaneously meet the needs of patients, carers and healthcare staff. It discusses the challenges associated with the translation of this narrative description of a systems approach (What?) into a practical implementation guide or toolkit (How?), supported by evidence of its effective use in health and care improvement practice. Finally, the paper reflects on the lessons to be learned from this process and their possible repercussions for design research and the practice of design.

A Review of Changeability in Complex Engineering Systems

Dynamic pressures and calls by stakeholders for systems to maintain value in the presence of change requires the rethinking of how system value is provided. A successful approach to cope with these pressures is to design engineering systems that can be changed easily. However, to identify suitable aspects of change, one must be able to analyze the changeability of engineering systems. This paper introduces the roles and impacts of changeability in the design of complex engineering systems through a review of literature in the field. The literature conveys that changeability is suitable for assessing the effects of potential change and can support the effective exploration of design space as a means of maintaining stakeholder value throughout a systems lifecycle.

Virtual prototyping for maritime crane design and operations

This paper presents the implementation of the virtual prototyping system for maritime crane design and operations. The study is designed to bridge the following gaps in maritime crane system simulations. First, the virtual prototyping system introduces an open and flexible platform oriented to overall product and system design, modeling, simulation and visualization. Second, the virtual simulator for operations based on the proposed framework is reinforced by high fidelity models of physics and dynamics. The paper discusses the challenges in virtual prototyping of complex multi-domain systems from the perspectives of modern complex engineering system design, modeling and simulation of multi-domain dynamic systems, large set of data exchange for communication and visualization. The software architecture of the system is based on the application of the functional mock-up interface standard. This utilizes the current available modeling and simulation tools, and allows for the exchange and reuse of models. Simulations in a virtual environment permit the evaluation of multiple trade-offs and alternative solutions from early design stages. As a case study and verification, the knuckle boom crane systems were implemented. The virtual crane simulator proved the effectiveness of the proposed virtual prototyping system in solving the long-existing challenges in simulations of complex multi-domain systems.

Ontology-Based Representation of Design Decision Hierarchies

The design of complex engineering systems requires that the problem is decomposed into subproblems of manageable size. From the perspective of decision-based design (DBD), typically this results in a set of hierarchical decisions. It is critically important for computational frameworks for engineering system design to be able to capture and document this hierarchical decision-making knowledge for reuse. Ontology is a formal knowledge modeling scheme that provides a means to structure engineering knowledge in a retrievable, computer-interpretable, and reusable manner. In our earlier work, we have created ontologies to represent individual design decisions (selection and compromise). Here, we extend the selection and compromise decision ontologies to an ontology for hierarchical decisions. This can be used to represent workflows with multiple decisions coupling together. The core of the proposed ontology includes the coupled decision support problem (DSP) construct, and two key classes, namely, Process that represents the basic hierarchy building blocks wherein the DSPs are embedded, and Interface to represent the DSP information flows that link different Processes to a hierarchy. The efficacy of the ontology is demonstrated using a portal frame design example. Advantages of this ontology are that it is decomposable and flexible enough to accommodate the dynamic evolution of a process along the design timeline.

An adaptive sampling method for variable-fidelity surrogate models using improved hierarchical kriging

ABSTRACTVariable-fidelity (VF) modelling methods have been widely used in complex engineering system design to mitigate the computational burden. Building a VF model generally includes two parts: design of experiments and metamodel construction. In this article, an adaptive sampling method based on improved hierarchical kriging (ASM-IHK) is proposed to refine the improved VF model. First, an improved hierarchical kriging model is developed as the metamodel, in which the low-fidelity model is varied through a polynomial response surface function to capture the characteristics of a high-fidelity model. Secondly, to reduce local approximation errors, an active learning strategy based on a sequential sampling method is introduced to make full use of the already required information on the current sampling points and to guide the sampling process of the high-fidelity model. Finally, two numerical examples and the modelling of the aerodynamic coefficient for an aircraft are provided to demonstrate the approximation capability of the proposed approach, as well as three other metamodelling methods and two sequential sampling methods. The results show that ASM-IHK provides a more accurate metamodel at the same simulation cost, which is very important in metamodel-based engineering design problems.

A study on the Requirements to Support the Accurate Prediction of Engineering Change Propagation

ABSTRACTThis paper builds on previous work in the area of design representation to identify the types of modeling information required to support the accurate prediction of engineering change propagation during the design of complex engineering systems. It uses the Function–Behavior–Structure (FBS) framework as a basis to examine how engineering changes can occur and how they may subsequently propagate. It was revealed that the relationship between change requirements and product components, the functional dependencies between product components, the behavioral dependencies between product components, and the structural dependencies between product components are all essential modeling information that need to be considered to fully describe the propagation of engineering changes. The essential modeling information identified can serve as modeling requirements to support the development of change prediction methods. In this work, three example design methods were discussed to examine the feasibility of using the modeling requirements for method evaluation. It was concluded that the level of accuracy achievable depends on the design context and hence a direct comparison between methods developed for different design contexts may not be meaningful. This suggests that accuracy should not be used as the sole criterion to evaluate change prediction methods.

Data driven uncertainty evaluation for complex engineered system design

Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate structure decomposition, which limits their practical application. The rapid expansion of data makes utilizing data to guide and improve system design indispensable in practical engineering. In this paper, a data driven uncertainty evaluation approach is proposed to support the design of complex engineered systems. The core of the approach is a data-mining based uncertainty evaluation method that predicts the uncertainty level of a specific system design by means of analyzing association relations along different system attributes and synthesizing the information entropy of the covered attribute areas, and a quantitative measure of system uncertainty can be obtained accordingly. Monte Carlo simulation is introduced to get the uncertainty extrema, and the possible data distributions under different situations is discussed in detail. The uncertainty values can be normalized using the simulation results and the values can be used to evaluate different system designs. A prototype system is established, and two case studies have been carried out. The case of an inverted pendulum system validates the effectiveness of the proposed method, and the case of an oil sump design shows the practicability when two or more design plans need to be compared. This research can be used to evaluate the uncertainty of complex engineered systems completely relying on data, and is ideally suited for plan selection and performance analysis in system design.

An end-user decision model with information representation for improved performance and robustness in complex system design

This paper demonstrates how system performance and robustness can be improved by incorporating models of end-user decision making in the early stages of complex engineered system design. End-user decision making is incorporated into the system design models through the novel use of decision and game theory methods. Methods for representing, dispersing, and absorbing information by end users in engineered system models are explored. The benefits of using mechanism design of end-user information to manipulate end-user decisions, with the goal of improving system performance, are examined. Further, incorporation of end-user models is shown to improve system robustness with respect to variations in user population. Complex system examples of aircraft and buildings are explored as test beds that showcase the benefits of incorporating an end-user decision model in the early stages of design.

On Risk-Based Design of Complex Engineering Systems: An Analytical Extreme Event Framework

In this work, a novel analytical framework is proposed for the risk-based design of complex engineering systems. The risk based-design process is the reverse process of risk propagation that entails the optimum allocation of the desired risk of failure of the system to all of its components. This paper studies the challenges in the design process of complex systems, the mathematical modeling of their topological architecture, and their unique behaviors. These characteristics make it impossible for the designer to use the common reliability and risk methodologies in the design process. The fundamental development of this work is an analytical upper bound for the distribution of risk of failure in the subsystem or element level. This upper bound satisfies the subadditivity condition of a coherent measure of risk. Additionally, its simplicity and low computational cost provide an appropriate framework as a fundamental building block for the risk-based design of complex systems. The proposed methodology is applied to three examples with insignificant desired probability of failure and its accuracy is compared with the Monte Carlo simulation, demonstrating its effectiveness and value.

Resiliency analysis for complex engineered system design

AbstractResilience is a key driver in the design of systems that must operate in an uncertain operating environment, and it is a key metric to assess the capacity for systems to perform within the specified performance envelop despite disturbances to their operating environment. This paper describes a graph spectral approach to calculate the resilience of complex engineered systems. The resilience of the design architecture of complex engineered systems is deduced from graph spectra. This is calculated from adjacency matrix representations of the physical connections between components in complex engineered systems. Furthermore, we propose a new method to identify the most vulnerable components in the design and design architectures that are robust to transmission of failures. Nonlinear dynamical system and epidemic spreading models are used to compare the failure propagation mean time transformation. Using these metrics, we present a case study based on the Advanced Diagnostics and Prognostics Testbed, which is an electrical power system developed at NASA Ames as a subsystem for the ramp system of an infantry fighting vehicle.

Visual analytics for early-phase complex engineered system design support.

This article reports on our ongoing experiences in developing visual analytics tools for real-world CESs. Our work focuses on the early design phase during which a large design space is explored, poor alternatives are pruned, and valuable alternatives are considered further. Visual analytics tools can provide interactive discovery, exploration, and understanding of real-world complex engineered systems (CES). The proposed tool, which focuses on the early design phase, can help users perform routine CES design analysis tasks and offer stakeholder-specific visual representations of complex design models.

An Enhanced Analytical Target Cascading and Kriging Model Combined Approach for Multidisciplinary Design Optimization

Multidisciplinary design optimization (MDO) has been applied widely in the design of complex engineering systems. To ease MDO problems, analytical target cascading (ATC) organizes MDO process into multilevels according to the components of engineering systems, which provides a promising way to deal with MDO problems. ATC adopts a coordination strategy to coordinate the couplings between two adjacent levels in the design optimization process; however, existing coordination strategies in ATC face the obstacles of complicated coordination process and heavy computation cost. In order to conquer this problem, a quadratic exterior penalty function (QEPF) based ATC (QEPF-ATC) approach is proposed, where QEPF is adopted as the coordination strategy. Moreover, approximate models are adopted widely to replace the expensive simulation models in MDO; a QEPF-ATC and Kriging model combined approach is further proposed to deal with MDO problems, owing to the comprehensive performance, high approximation accuracy, and robustness of Kriging model. Finally, the geometric programming and reducer design cases are given to validate the applicability and efficiency of the proposed approach.

Assisting Decisions in Inventive Design of Complex Engineering Systems

Adopting relevant decisions in engineering design of multidisciplinary problematic is often perceived as a critical issue in industry. This situation becomes even more critical when in inventive design context since decisions lead R&D teams towards unusual directions that, by nature, expose the company to risky investments. IDM-TRIZ is an advanced framework proposed to address the issue of representing multidisciplinary and complex situations to facilitate TRIZ use in inventive design projects. One of its tool “problem graph” has been designed to link expert knowledge representations and automatic extraction of populations of contradictions. This first step nevertheless presents a limitation; it doesn’t help project leaders to properly estimate the incidences of their decisions when engaging inventive activities to solve a specific problem. This paper addresses this issue and proposes an enhanced methodological process that is handling dynamic multiple graphical representations based on computed project data's exploitation. It provides relevant proofs that, in comparison with a traditional TRIZ approach, we have significantly improved the robustness of R&D decisions. In addition, we illustrate our methodology using a case study conducted in airspace industry (helicopter assembly complexity) to present and validate our hypothesis.

Application of a surrogate modeling to the ship structural design

Multi-criteria design of complex engineering systems is methodologically and computationally demanding and time consuming process. Optimization algorithms, problem decomposition, surrogate modeling and methods for decision support are synthesis methods that are usually employed to solve the design problem. The objective of the research presented in this paper was to improve the design process of complex thin-walled ship structures through an application of surrogate modeling on this type of a problem. Methods considered are integrated into interactive computing environment for multi-criteria design in order to enable effective and efficient interaction between designer and design problem within given design timeframe. Surrogate modeling was applied for the approximation of structural responses in order to reduce a number of finite element method (FEM) calculations inside of optimization loop. Applicability of the proposed approach and suitability of different surrogate modeling methods for that purpose is extensively tested on the simple barge example.

A Distributed Pool Architecture for Highly Constrained Optimization Problems in Complex Systems Design

Optimal design of complex engineering systems is challenging because numerous design variables and constraints are present. Dynamic changes in design requirements and lack of complete knowledge of subsystem requirements add to the complexity. We propose an enhanced distributed pool architecture to aid distributed solving of design optimization problems. The approach not only saves solution time but is also resilient against failures of some processors. It is best suited to handle highly constrained design problems, with dynamically changing constraints, where finding even a feasible solution (FS) is challenging. In our work, this task is distributed among many processors. Constraints can be easily added or removed without having to restart the solution process. We demonstrate the efficacy of our method in terms of computational savings and resistance to partial failures of some processors, using two mixed integer nonlinear programming (MINLP)-class mechanical design optimization problems.

Vector optimization of laser solid freeform fabrication system using a hierarchical mutable smart bee-fuzzy inference system and hybrid NSGA-II/self-organizing map

The purpose of current investigation is to develop a robust intelligent framework to achieve efficient and reliable operating process parameters for laser solid freeform fabrication (LSFF) process as a recent and ongoing topic of investigation. Firstly, based on mutable smart bee algorithm (MSBA) and fuzzy inference system (FIS) two models are developed to identify the clad hight (deposited layer thickness) and the melt pool depth as functions of scanning speed, laser power and mass powder. Using the obtained model, the well-known multiobjective evolutionary algorithm called non-dominated sorting genetic algorithm (NSGA-II) is used for multi-criterion optimization of LSFF process. According to the available reported information and also the author's experiments, it is observed that the obtained Pareto front is not justifiable since it fails to cover the entire Pareto hyper-volume due to the lack of intensified exploration. To tackle this deficiency, authors execute a post optimization process through utilizing a competitive unsupervised machine learning approach known as self-organizing map (SOM) with cubic spatial topology. Achieved results indicate that this grid based network is capable of enhancing the intensification of Pareto solutions since its synaptic weights successfully imitate the characteristics of non-dominated solutions (optimal values of mass powder, laser power and scanning speed). For extracting the corresponding objective functions of these non-dominated synaptic weights, MSBA---FIS is used again to map the operating parameters to objective functions space. After the termination of abovementioned procedures, a valuable archive, containing a set of non-dominated solutions, is obtained which lets the authors to make a deliberate engineering trade-off. Simulation experiments reveal that the proposed intelligent framework is highly capable to cope with complex engineering systems. Besides, it is observed that MSBA is more efficient in evolving the structure of hierarchical fuzzy inference system in comparison with classic hierarchical GA-FIS model. This rises from the simple structure of MSBA that turns it into a fast and robust algorithm for handling constraint distributed systems (i.e. hierarchical FIS in current investigation). The obtained results also indicate that the introduced intelligent framework is applicable for optimal design of complex engineering systems where there exists no analytical formulation that describes the phenomenon as well as information of optimal operating parameters.

On the Coordination of Multidisciplinary Design Optimization Using Expert Systems

In the design of complex engineering systems involving multiple disciplines it is critical that the interactions between the subsystems of the problem are accounted for. Only by considering the fully coupled system can an optimal design emerge.

Implementation and Validation of an Improved Collaborative Optimization Method

Collaborative optimization (CO) is the most widely used Multidisciplinary design optimization (MDO) method for the design of complex engineering system. But some serious computational difficulties are found in its application. Reasons that cause computational difficulties in original CO were analyzed and a new improved collaborative optimization method (ICO) was presented. The L1 norm was used to improve subsystem consistency constraint and to avoid discontinuities in subsystem object function derivatives. Penalty function was added to system-level object function to convert constrained optimization into unconstrained optimization. A quick-start strategy was used to make the best use of optimal solution of system-level optimization in subsystem-level optimization. Experimental results show that the robustness, reliability and computing efficiency of ICO are higher than CO.