Engineering Design Optimization Research Articles

Investigation of the Critical Factors Influencing Multi-Stakeholders’ Participation in Design Optimization of EPC Projects

Design optimization can influence the achievement of management goals and the sustainable development of EPC (engineering–procurement–construction) projects. Current research regarding engineering design optimization mainly focuses on the technology aspect, while lacking extensive attention regarding the factors influencing stakeholders’ participation in design optimization of EPC projects. Based on the existing literature and expert opinions, this study identifies 33 critical influencing factors and adopts the DEMATEL (decision-making trial and evaluation laboratory) and ISM (interpretive structural model) method to analyze the hierarchical structure and interrelationships among these factors. The results show that the factors, including subcontractors’ participation during the design, design management level, performance evaluation mechanism, technological development, owners’ attitude towards disputes, and sensitivity to project cost growth, play critical roles in multi-stakeholders’ participation in design optimization of EPC projects. All these factors can be divided into causal factors (13) and result factors (20) and a hierarchical structure model is developed for the whole system, composed of three types of influencing factors, that is, the surface direct factor, intermediate indirect factor, and deep-rooted factor. The findings of this study can help managers to have a better understanding of design optimization of EPC projects from the stakeholder perspective and help managers to take effective measures to improve the status quo as well as facilitate the sustainable development of this kind of project.

Improved Harris Hawks optimization for global optimization and engineering design

Improved Harris Hawks optimization for global optimization and engineering design

A versatile volume-based modeling technique of distributed local quadratic convergence for aeroengines

For advanced aero-engine design and research, modeling and simulation in a digital environment is indispensable, especially for engines of complicated configurations, such as variable cycle engines (VCE) and adaptive cycle engines (ACE). Also, in the research of future smart engines, reliable real-time digital twins are paramount. However, the 2 dominant methods that used in solving the simulation models, Newton-Raphson (N-R) method and volume-based method, are not fully qualified for the study requirements, because neither of them reaches the satisfactory balance of convergence rate and calculating efficiency. In this study, by deeply analyzing the mathematical principle of these 2 methods, a novel modeling and solving method for aero-engine simulation, which integrates the advantages of both N-R and volume-based methods, is established. It has distributed architecture and local quadratic convergence rate. And a novel modeling method for variable area bypass injectors (VABI) is put forward. These facilitate simulation of various configurations of aero-engines. The modeling cases, including a high bypass-ratio (BPR) turbofan and an ACE, illustrate that the novel technique decreases the iterations by about two-thirds comparing with volume-based method, while the success rate of convergence remains over 99%. This proves its superiority in both convergence and calculating efficiency over the conventional ones. This technique can be used in advanced gas turbine engine design and control strategy optimization, and study of digital twins.

Truss optimization with frequency constraints using the medalist learning algorithm

Though the truss optimization with frequency constraints is very complicated due to its high nonlinear property, the importance and necessity of truss optimization in the real engineering design render it attractive for many researchers. This paper proposed a new heuristic algorithm inspired by the observation of the learning behavior of individuals in a group for the truss optimization with frequency constraints. The new swarm intelligent heuristic algorithm called the medalist learning algorithm has a concise implementation procedure composed of two key operations: identifying the medalists and conducting the individual learning. The learning period is divided into predefined learning stages. In each learning stage, a learning efficiency is generated using a learning function originated from the natural learning function. The key operations mentioned above are repeated in each learning stage. For an excellent performer called medalist, to conduct individual learning is to carry out self-learning trials; but for a common learner, to conduct individual learning is to learn from his/her past experience and imitate the medalists. Six well-known truss optimization problems were employed to verify the efficiency of the new algorithm. The results obtained by the medalist learning algorithm were compared with those previously reported in literature. The new algorithm outperforms the others in terms of the best and average weights. The computational cost measured by the number of the function estimations of the structure for the new algorithm to obtain the best design usually is in the middle of the reported costs. In some cases, the new algorithm has the lowest worst weight and smallest number of function estimations.

Multi-fidelity data-driven optimization design framework for self-piercing riveting process parameters

Due to its excellent performance, the self-piercing riveting (SPR) process has been widely used in auto body manufacturing in recent years. The need for efficient and reliable design of SPR process parameters is growing in industry. Traditional trial-and-error design methods rely on historical experience and can be limited by physical experimental materials and equipment. Data-driven methods rely on data accuracy and data volume, and data from a single source is often inadequate to meet design needs. To address these issues, a multi-fidelity data-driven optimization design framework is proposed in this paper. The most significant feature of this framework is the fusion of physical experiment data and simulation data to build surrogate models. In this framework, a modified optimal Latin hypercube sampling method and a multi-fidelity surrogate model based on transfer learning and neural networks are proposed for SPR process. This multi-fidelity surrogate model can use a very small amount of experiment data to modify the surrogate model built on simulation data based on transfer learning, so that the model predictions can more closely match the physical experiment results at a lower modeling cost. Benefit from this method, the proposed framework can balance the contradiction between design accuracy and development cost compared to a single-fidelity data-driven framework. The application cases show that the prediction errors of the multi-fidelity models are <0.1 mm for the key geometric parameters of the SPR joint. As verified by physical experiments, the rivets and dies selected by the framework are the optimal solutions within the optional range. To the authors' knowledge, this is the first time that a multi-fidelity modeling method has been introduced to the field of SPR process, to solve the process parameter optimization design problem. Further, the method is not limited to the use of SPR process but can be applied as a paradigm in other engineering optimization design problems, especially in joining process parameter design problems.

An Iterative Two-Stage Multifidelity Optimization Algorithm for Computationally Expensive Problems

Engineering design optimization often involves use of numerical simulations to assess the performance of candidate designs. The simulations for computing high-fidelity (HF) performance estimates, such as finite element analysis or computational fluid dynamics, are typically computationally expensive. In some cases, it may also be possible to run an alternate or cheaper version of the simulation (through, e.g., use of a coarse mesh) to yield a low-fidelity (LF) performance estimate. Multi-fidelity optimization refers to the class of methods that aim to manage LF and HF evaluations efficiently to optimize computationally expensive problems within a limited computing budget. Among the prominent existing multi-fidelity approaches, some of them depend on a sufficiently dense a priori sampling; while others use unidirectional information exchange from LF to HF; both of which lead to a possibility of spending evaluation budget on unpromising search regions. This paper proposes an improved multi-fidelity approach using an iterative, two-stage scheme (MFITS). It uses the collective information from previously evaluated designs to determine a sampling neighbourhood for LF evaluations. These samples are in-turn used for building a co-kriging surrogate model that is then searched globally to identify a good candidate for HF evaluation. By restricting the LF sampling neighbourhood, the computational budget can be used more efficiently, as the search is focused on regions that have historically produced good quality solutions. Numerical experiments and benchmarking are conducted on two suites of test problems and two practical design optimization problems to demonstrate the efficacy of MFITS.

Key technology and application of gun agile optimization design based on dynamic simulation

The new generation of artillery equipment is showing the development trend of lightweight, long-range, intelligent et al. However, the traditional development mode based on physical prototype test cannot meet these needs of equipment development. In the development, there are some problems, such as lack of systematic guidance in the overall design scheme, low degree of digitalization and parameterization of the model, and long period of serial iterative development, which need to be solved by optimization design. Parametric modeling technology, hard point driven template model technology and structural optimization design technology are briefly introduced, which are the foundation of equipment optimization design. These key technologies, combined with the overall scheme design, chassis system design and ammunition loading timing optimization design of a large-caliber tracked self-propelled gun, showing the basic development route of artillery performance improvement. The optimization design of artillery type equipment are summarized in recent years, which provide reference for other similar work.

Sensitivity analysis of numerical model parameters for optimized PEH responses

With the increasing popularity of wearable devices, typically employed in fitness and health monitoring, there is an evident need to extend their autonomy and replace the conventional power sources with environmentally friendly alternatives. Piezoelectric energy harvesting systems, optimized for collecting kinetic energy from random human motion and transduce it into electrical energy, represent a viable option for powering autonomous wearables. Since established analytical methods are unable to model the behaviour of piezoelectric harvesters with complex optimized geometries, suitable numerical models need to be employed for their design. This implies the need of a thorough study focused on the mechanical engineering design optimization purposes of how the finite element type and mesh density affect the uncoupled modal and coupled transient responses of a new class of optimised design configurations of the studied devices.

Revealing contribution mechanisms of project managers' technical competencies toward success in oil and gas projects

PurposeProject managers' competencies contribute toward project success in a considerable manner. The significance of technical competencies' (TCs) contribution toward success in Oil and Gas (O&G) projects is even higher due to the complexity and the technology-intensive nature of these projects. However, technical competencies have often been overlooked in favor of behavioral competencies or simply identified and listed in terms of their significance. Thus, there is currently very limited research-based knowledge concerning the contribution mechanisms of technical competencies toward project execution success. Hence, the aim of this study is to explore the contribution mechanisms of TCs toward success in O&G projects.Design/methodology/approachA case study was employed as the research strategy through which data was collected from project professionals in O&G projects in the Kingdom of Bahrain using semi-structured interviews. Transcripts of the interviews were then analyzed through thematic analysis method with the aid of NVIVO 12.FindingsThe findings reveal six mechanisms through which technical competencies of project managers contribute toward execution success in O&G projects. TCs not only act as a launch pad toward project success, but also help in optimizing engineering designs throughout the project life cycle.Originality/valueInstead of simply identifying and listing TCs, the obtained results enhance our current understanding of their contribution mechanisms toward project success in O&G projects.

Applications of multi-fidelity multi-output Kriging to engineering design optimization

Surrogate modelling is a popular approach for reducing the number of high fidelity simulations required within an engineering design optimization. Multi-fidelity surrogate modelling can further reduce this effort by exploiting low fidelity simulation data. Multi-output surrogate modelling techniques offer a way for categorical variables e.g. the choice of material, to be included within such models. While multi-fidelity multi-output surrogate modelling strategies have been proposed, to date only their predictive performance rather than optimization performance has been assessed. This paper considers three different multi-fidelity multi-output Kriging based surrogate modelling approaches and compares them to ordinary Kriging and multi-fidelity Kriging. The first approach modifies multi-fidelity Kriging to include multiple outputs whereas the second and third approaches model the different levels of simulation fidelity as different outputs within a multi-output Kriging model. Each of these techniques is assessed using three engineering design problems including the optimization of a gas turbine combustor in the presence of a topological variation, the optimization of a vibrating truss where the material can vary and finally, the parallel optimization of a family of airfoils.

Artificial ecosystem optimization by means of fitness distance balance model for engineering design optimization

Optimization techniques have contributed to significant strides in complex real-world engineering problems. However, they must overcome several difficulties, such as the balance between the capacities for exploitation and exploration and avoiding local optimum. An enhanced Artificial Ecosystem Optimization (AEO) is proposed incorporating Fitness Distance Balance Model (FDB) for handling various engineering design optimization problems. In the proposed optimizer, the combined FDB design aids in selecting individuals who successfully contribute to population-level searches. Therefore, the FDB model is integrated with the AEO algorithm to increase the solution quality in nonlinear and multidimensional optimization situations. The FDBAEO is developed for handling six well-studied engineering optimization tasks considering the welded beam, the rolling element bearing, the pressure vessel, the speed reducer, the planetary gear train, and the hydrostatic thrust bearing design problems. The simulation outcomes were evaluated compared to the systemic AEO algorithm and other recent meta-heuristic approaches. The findings demonstrated that the FDBAEO reached the global optimal point more successfully. It has demonstrated promising abilities. Also, the proposed FDBAEO shows greater outperformance compared to several recent algorithms of Atomic Orbital Search, Arithmetic-Trigonometric, Beluga whale, Chef-Based, and Artificial Ecosystem Optimizers. Moreover, it declares great superiority compared to various reported optimizers.

Harmonizing the use of optimization and feedback in process operations and control

This paper addresses how to combine the two main engineering design tools, optimization and feedback, to obtain high performance process control or process operation systems. We discuss the historical development of process control, where feedback dominated the early practical designs, and optimization was then incorporated much later through technologies like model predictive control. In chemical production scheduling, on the other hand, optimization had a strong early influence on the problem formulation, and feedback has only recently made an appearance. The recent developments in both process control and scheduling are illustrated with specific examples emerging from this series of FOCAPO/CPC meetings. The paper next presents recent theoretical developments in nominal and stochastic model predictive control. The closed-loop properties that arise from these different open-loop optimal control problems are then compared. The paper closes with some discussion of when the improvements of the closed-loop properties are worth the added complexity of the stochastic optimal control problem.

Understanding hydrogen jet dynamics for direct injection hydrogen engines

The energy paradigm is shifting toward carbon-free and low-emission alternative fuels. Among the many candidates, hydrogen is considered a promising option thanks to its favorable fuel properties, including zero carbon content, high gravimetric energy density, and fast flame speed, despite its technical fuel production and storage challenges. Direct injection technology has been identified as the best approach for applying hydrogen in combustion engines to overcome the accompanying issues, such as backfire and low energy density. However, studies have yet to be conducted on hydrogen jet behavior in direct injection systems, specifically, studies that elucidate engine design and injection parameter optimization in the combustion system. Therefore, we aimed to assess hydrogen jet behavior through experiments and computational approaches comprehensively. Hydrogen was injected at 10 MPa using the direct injection method into a constant volume chamber under a quasi-steady ambient condition. Z-type high-speed Schlieren imaging was performed using a high-speed camera to visualize the hydrogen jet structure, which depicted the hydrogen jet’s vapor intermittency and vortex structure. We also performed a computational fluid dynamics (CFD) simulation to understand the aerodynamics of the jet. The results showed the formation of vortical flow in the inner core region, where the pressure was comparatively lower than that on the outer side of the jet. Our further investigation of the injection strategy showed that multiple injections were more beneficial in forming a favorable hydrogen-air mixture near the spark plug than a single injection case.

Back analysis of geomechanical parameters for rock mass under complex geological conditions using a novel algorithm

Geomechanical properties of rock mass are of vital importance for design optimization and stability evaluation in geotechnical engineering. Traditionally, they are estimated by laboratory experiments, in situ tests, and empirical classification, which ignore the scale effect, and may be not representative, leading to uncertainties of parameters. Back analysis method based on the monitoring displacement data provides the state-of-the-art tool for the estimation of geomechanical parameters. This paper proposed a novel back analysis method, which combined the optimized particle swarm optimization (OPSO) algorithm with the support vector machine (SVM) algorithm. And then, we cooperate the method with finite element method (FEM) forming the comprehensive method named OPSO-SVM-ABAQUS. This method has developed the standard PSO algorithm from two aspects for more powerful optimization capability and higher optimization precision. The OPSO algorithm is then used for the estimation of the SVM hyperparameters and the optimal values of geomechanical parameters. Furthermore, the performance of the proposed algorithm is compared with other six algorithms. Finally, the geomechanical parameters of the interlayered soft and hard rocks are calculated accurately, with the average error between the back-analyzed and assumed actual parameters to be 0.91 %. And the parametric investigations are implemented with the trained model. It dominates that the proposed algorithm is feasible for reasonable parameters in geotechnical engineering under complex geological conditions.

Letter: Application of optimization algorithms to engineering design problems and discrepancies in mathematical formulas

Engineering design optimization problems have attracted the attention of researchers since they appeared. Those who work on developing optimization algorithms, in particular, apply their developed algorithms to these problems in order to test their new algorithms’ capabilities. The mathematical discrepancy emerges during the implementation of equations and constraints related to these engineering problems. This is due to an error occurring in writing or transmitting these equations from one paper to another. Maintaining these discrepancies will have a negative impact on the assessment and model performance verification of the newly developed algorithms, as well as the decision-making process. To address this issue, this study investigates the mathematical discrepancies occurred by researchers in four well-known engineering design optimization problems (Welded Beam Design WBD, Speed Reducer Design SRD, Cantilever Beam Design CBD, and Multiple Disk Clutch Brake Design MDCBD). We have investigated some of the recently published papers in the literature, identifying discrepancies in their mathematical formulas, and fixing them appropriately by referring and comparing them to the original problem. Furthermore, all mathematical discrepancies , references, parameters, cost functions, constraints, and constraint errors are highlighted, arranged and organized in tables. As a result, this work can help readers and researchers avoid being confused and wasting time when working on these engineering design optimization problems.

Influence of the flow area around the ball valve on the flow characteristics of the injector control valve

The increase in common rail pressure can lead to increased cavitation inside the injector, resulting in degradation of injector performance and reduced life. The paper investigates the effect of the pressure block structure parameters (initial flow area around the ball valve) on the velocity field, pressure field, fuel gas phase volume fraction and drain rate of the control valve. The relationship between the initial flow area around the ball valve on the cavitation strength and unloading rate inside the valve was revealed. The results show that both the reduction of the flow area around the ball valve and the increase of the cavitation intensity inhibit the rate of oil discharge from the control valve. The reduction of the fuel flow area inhibits the expansion of the low-pressure region (0–1 MPa) within the flow layer, thus limiting the development of cavitation. The reduction of the cavitation area increases the fuel flow rate, however, the increase in flow rate increases the cavitation phenomenon, and these changes form a cycle (Reviewer 5. comment 2). The increase in cavitation inhibits the control valve pressure relief rate more significantly than the decrease in the initial flow area around the ball valve. Based on this, a stepped-pressure block model is proposed. The stepped pressure block model can effectively reduce the cavitation strength near the seal and enhance the oil discharge rate of the control valve. The study can provide a reference for the engineering optimization design of high-pressure common rail injector control valves.

A multi-strategy enhanced African vultures optimization algorithm for global optimization problems

Abstract The African vultures optimization algorithm (AVOA) is a recently proposed metaheuristic inspired by the African vultures’ behaviors. Though the basic AVOA performs very well for most optimization problems, it still suffers from the shortcomings of slow convergence rate and local optimal stagnation when solving complex optimization tasks. Therefore, this study introduces a modified version named enhanced AVOA (EAVOA). The proposed EAVOA uses three different techniques namely representative vulture selection strategy, rotating flight strategy, and selecting accumulation mechanism, respectively, which are developed based on the basic AVOA. The representative vulture selection strategy strikes a good balance between global and local searches. The rotating flight strategy and selecting accumulation mechanism are utilized to improve the quality of the solution. The performance of EAVOA is validated on 23 classical benchmark functions with various types and dimensions and compared to those of nine other state-of-the-art methods according to numerical results and convergence curves. In addition, three real-world engineering design optimization problems are adopted to evaluate the practical applicability of EAVOA. Furthermore, EAVOA has been applied to classify multi-layer perception using XOR and cancer datasets. The experimental results clearly show that the EAVOA has superiority over other methods.

Ọdịgbo Metaheuristic Optimization Algorithm for Computation of Real-Parameters and Engineering Design Optimization

This paper proposes a new population-based global optimization algorithm, Ọdịgbo Metaheuristic Optimization Algorithm–ỌMOA, for solving complex bounded-constraint/single objective real-parameter problems found in most engineering and scientific applications. It’s inspired by the human socio-cultural informal discipleship learning pattern inherent in the behavior of the Ndịgbo peoples; the subject – primary (Nwa-ahịa), in mercantile cycle grows to a secondary (Mazi) owing to the intuitive stratagem (dialect - Ịgba) embedded in an aged-long cultural model “Ịgba-ọsọ-ahịa” (meaning, strategic marketing skills, and practice). The model mimics the search routine for satisfying a customer’s need in the market, built into exploration and exploitation applied in the mathematical model. About 30 complex classical unconstrained functions are tested, comparing results with that of five similar state-of-the-art algorithms. Also, 29 CEC-2017 single objective real constraint benchmark serious dimensional problems were simulated and compared against the winners of that competition. Validation includes statistical (t-test, p-value) comparison and for 50 Dimension constraint problems as ỌMOA demonstrated superior performance. TCS (9.18%), WBP (6.3%), PVDP (601%), RGP (319%), RBP (760%), GTCD (202%), HIMMELBLAU (4%), and CDP (88.12%) are the improvements made on 8 CEC-2020 engineering real design problems against the former best performances; OMOA is simple to implement, replicate and applicable across domains. Also, some new, improved optimum was obtained in Shubert and Schaffer 4 function compared to the global optimums.

Effects of Queue Jump Lane on Public Transport: Case Study Using Microsimulation

Abstract Public transport priority is an essential transport engineering technique for the transition to sustainable urban mobility. This article presents a case study that simulates the effects of introducing a queue jump lane followed by a bus lane beyond an intersection compared to a dedicated lane only in the section beyond the intersection (without introducing a queue jump lane). The monitored parameters were speed and reliability of public transport. The simulation was performed using the PTV Vissim program. The case study was evaluated in terms of public transport operation, as well as selected effects on other road traffic with the aim to determine the optimal traffic engineering design and improve public transport operation in practice. This case study provides valuable insights into the effects of these priority measures. The results can contribute to the improvement of public transport operations.

A Novel Meta-Heuristic Algorithm for Numerical and Engineering Optimization Problems: Piranha Foraging Optimization Algorithm (PFOA)

This paper provides a novel meta-heuristic optimization algorithm for solving continuous optimization problems efficiently in the field of numerical and engineering optimization: Piranha Foraging Optimization Algorithm (PFOA). The algorithm is inspired by the flexible and mobile foraging behaviour of piranha swarm and divides their foraging behavior into three patterns: localized group attack, bloodthirsty cluster attack and scavenging foraging, simulates the above behaviors to construct two dynamic search processes for exploration and exploitation. PFOA uses three strategies of non-linear parameter control, population survival and reverse evasion search to enable populations to have better population diversity at different stages of the search and to help find better solutions. To gain insight into the performance of PFOA, visualization methods were used to assess the efficiency of PFOA optimization and to analyse the impact of the characteristics of the three foraging modes, the sensitivity of the parameters and the size of the piranha population on the algorithm. The algorithm performance was further tested with 27 CEC benchmark functions and four real engineering design optimization problems, and the results were compared with 13 well-known meta-heuristics. Test results based on statistical methods such as box-line plots, Wilcoxon rank sum test and Friedman test in multiple dimensions (30, 50, 100 and fixed dimensions) show significant differences compared to other algorithms and that the performance of the algorithm is stable and in significant improvement. The unique advantages of PFOA in terms of the equilibrium of convergence speed and exploration can avoid getting trapped in local optimum regions and effectively solve optimization problems in complex search spaces.