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This paper aims to propose an optimization method to automatically adjust the spatial route of multibend pipes to meet the assembly demands in constrained space.

The compact geometric parameters that uniquely determine the pipe route are analyzed. Besides, the relationship between these parameters and the end pose is revealed based on the exponential product formula. Mathematical representations for the engineering constraints, including the end pose restriction, collision interference, manufacture ability and geometric limitations, are further established. On this basis, the adjustment of the spatial route is formulated as a multiconstraint optimization problem. A modified particle swarm optimization method based on the combination of gradient projection and swarm intelligence is designed to find the near-optimal pipe that meets the required assembly demands.

The experimental results show that the proposed method can effectively find the feasible pipe route that satisfies the engineering constraints and the end pose requirement is highly guaranteed.

The proposed method can automate the geometric adjustment of multi-bend pipes to meet the actual assembly demands, which significantly reduces manual efforts and guarantees high accuracy. The results demonstrate the possibility of further applications in the pipe assembly or design process, especially in ships, aerospace products or pressure vessels.

The purpose of this paper is to investigate design alternatives for pump‐included water distribution networks considering sustainability and reliability aspects. The aim is to demonstrate that CO₂ emissions could be reduced at a reasonable cost. The paper also investigates the trade‐offs between cost and reliability of water distribution networks.

An existing genetic algorithm optimization tool is customized in this research to perform multi‐objective optimization with various objectives and constraints. The developed model is demonstrated using a benchmark water distribution network.

The results from this research suggest that CO₂ emissions from water distribution networks could be reduced at a reasonable cost by choosing better objectives during the design stage. High system reliability could also be ensured for the lifetime by paying reasonable additional cost. This research presents various design alternatives for an engineer to choose from.

The design of water distribution networks is a computationally complex process and often requires significant CPU time to arrive at an optimal solution. This problem is significant in the case of larger networks, especially when all the failed states need to be simulated. Simpler measures of reliability could be adopted in the future.

Although a significant amount of research had been undertaken in the area of optimal water distribution network design, only limited research includes environmental impacts as a design objective. This paper not only includes environmental aspects but also considers reliability. The model proposed in this research is a useful tool for engineers for considering various alternatives before choosing the best design.

With an increasingly fierce competition of the shipbuilding industry, advanced technologies and excellent management philosophies in the manufacturing industry are gradually introduced to domestic shipyards. The purpose of this study is to promote the lean management of Chinese ship outfitting plants by lean production strategy.

To promote the lean implementation of Chinese shipyards, the lean practice of ship-pipe part production is highlighted by lot-sizing optimization and strategic CONWIP (constant work-in-process) control. A nonlinear programming model is formulated to minimize the total cost of ship-pipe part manufacturing and the particle swarm optimization (PSO)-based algorithm is designed to resolve the established model. Besides, the pull-from-the-bottleneck (PFB) strategy is used to control ship-pipe part production, verified by Simulink simulation.

Results show that the proposed lean strategy of the programming model and strategic PFB control could assist Chinese ship outfitting plants to leverage competitive advantage by waste reduction and lean achievement. Specifically, the PFB double-loop control strategy shows better performance when there is high productivity and the PFB single-loop control outperforms at lower productivity scenarios.

To verify the effectiveness of the proposed lean strategy, a case study is performed to validate the formulated model. Also, simulation experiments realized by FlexSim software are conducted to testify results obtained by the constructed programming model.

Lean production management practice of the shipyard building industry is performed by the proposed lean production strategy through lot-sizing optimization and strategic PFB control in terms of ship-pipe part manufacturing.

As a kind of NP-hard combinatorial optimization problem, pipe routing design (PRD) is applied widely in modern industries. In the offshore oil and gas industry, a semi-submersible production platform is an important equipment for oil exploitation and production. PRD is one of the most key parts of the design of semi-submersible platform. This study aims to present an improved ant colony algorithm (IACO) to address PRD for the oil and gas treatment system when designing a semi-submersible production platform.

First, to simplify PRD problem, a novel mathematical model is built according to real constraints and rules. Then, IACO, which combines modified heuristic function, mutation mechanism and dynamical parameter mechanism, is introduced.

Based on a set of specific instances, experiments are carried out, and the experimental results show that the performance of IACO is better than that of two variants of ACO, especially in terms of the convergence speed and swarm diversity. Finally, IACO is used to solve PRD for the oil and gas treatment system of semi-submersible production platform. The simulation results, which include nine pipe paths, demonstrate the practicality and high-efficiency of IACO.

The main contribution of this study is the development of method for solving PRD of a semi-submersible production platform based on the novel mathematical model and the proposed IACO.

The design of optimum pipe and duct networks with available procedures is difficult, if not impossible. A more efficient procedure that will automatically produce the optimum design is required. Such a procedure is presented in this article. The design is formulated as a constrained nonlinear optimization problem. This problem is solved using a unique numerical optimization algorithm. The solution entails the calculation of the cross sectional dimensions of the ducts and pipes so that the life cycle cost of the network is minimized. The topology equations are derived using graph theory thereby allowing complex networks with loops to be designed numerically. A duct network consisting of a fan and 35 duct sections is designed according to certain specifications. Using the proposed procedure optimum designs were obtained within seconds on a 33 MHz 486 micro‐computer. The procedure was further applied to the optimization of a coal pipeline. It is shown that the optimized solution will cost 14% ($8 million) less than the previous design with conventional design techniques.

The purpose of this paper is to develop a new rectilinear branch pipe‐routing algorithm for automatic generation of rectilinear branch pipe routes in constrained spaces of aero‐engines.

Rectilinear branch pipe routing that connects multiple terminals in a constrained space with obstacles can be formulated as a rectilinear Steiner minimum tree with obstacles (RSMTO) problem while meeting certain engineering rules, which has been proved to be an NP‐hard and discrete problem. This paper presents a discrete particle swarm optimization (PSO) algorithm for rectilinear branch pipe routing (DPSO‐RBPRA) problems, which adopts an attraction operator and an energy function to plan the shortest collision‐free connecting networks in a discrete graph space. Moreover, this paper integrates several existing techniques to evaluate particles for the RSMTO problem in discrete Manhattan spaces. Further, the DPSO‐RBPRA is extended to surface cases to adapt to requirements of routing pipes on the surfaces of aero‐engines.

Pipe routing numeral computations show that, DPSO‐RBPRA finds satisfactory connecting networks while considering several engineering rules, which demonstrates the effectiveness of the proposed method.

This paper applies the Steiner tree theory and develops a DPSO algorithm to plan the aero‐engine rectilinear branch pipe‐routing layouts.

The objective of this study is to highlight the questions arising in the design of district heating and cooling systems (DHCSs) in a distributed generation context and to present a model to help find cost‐effective solutions.

Literature on energy systems optimisation is reviewed and a mixed integer programming model for decentralized DHCSs design is developed and applied to two real case studies.

Distributed cooling generation partly coupled with distributed cogeneration and DH is the preferred solution in the examined areas. The optimal configurations, with special reference to network sizing and layout, significantly depend on heating demand profiles and energy prices.

Interdependencies between energy units sizing and network layout definition should be considered. Obtaining more robust and reliable network configurations should be the objective of future modelling efforts.

Despite the growth of distributed energy conversion, designers often rely on centralized concepts in order to reap economies of scale. The presented model helps in discovering less usual solutions representing the most profitable option.

Combining and comparing central and distributed production of heat and cooling under consideration of network costs.

This paper aims to solve the problem that pipes conveying fluid are faced with severe reliability failures under the complicated working environment.

This paper proposes a dynamic reliability and variance-based global sensitivity analysis (GSA) strategy with non-probabilistic convex model for pipes conveying fluid based on the first passage principle failure mechanism. To illustrate the influence of input uncertainty on output uncertainty of non-probability, the main index and the total index of variance-based GSA analysis are used. Furthermore, considering the efficiency of traditional simulation method, an active learning Kriging surrogate model is introduced to estimate the dynamic reliability and GSA indices of the structure system under random vibration.

The variance-based GSA analysis can measure the effect of input variables of convex model on the dynamic reliability, which provides useful reference and guidance for the design and optimization of pipes conveying fluid. For designers, the rankings and values of main and total indices have essential guiding role in engineering practice.

The effectiveness of the proposed method to calculate the dynamic reliability and sensitivity of pipes conveying fluid while ensuring the calculation accuracy and efficiency in the meantime.

Computational fluid dynamics (CFD) technique is the most commonly used numerical approach to simulate fluid flow behaviour. Owing to its computationally, cost-intensive nature CFD models may not be easily and quickly deployable. In this regard, this study aims to present a support vector machine (SVM)-based metamodelling approach that can be easily trained and quickly deployed for carrying out large-scale studies.

Radial basis function and ε^*-insensitive loss function are used as kernel function and loss function, respectively. To prevent overfitting of the model, five-fold cross-validation root mean squared error is used while training the SVM metamodel. Rather than blindly using any SVM tuning parameters, a particle swarm optimisation (PSO) is used to fine-tune them. The developed SVM metamodel is tested using various error metrics on disjoint test data.

Using the SVM metamodel, a parametric study is conducted to understand the effect of various factors influencing the behaviour of the turbulent fluid flow in the pipe bend with CFD simulation data set. Based on the parametric study carried out, it is seen that the diametric position has the most effect on dimensionless axial velocity, whereas Reynolds number has the least effect.

This paper provides an effective PSO-tuned SVM metamodelling approach, which may be used as a significant cost-saving approach to quickly and accurately estimate fluid flow characteristics that, in general, require the use of expensive CFD models.

The paper deals with research activities to develop optimization workflows implying computational fluid dynamics (CFD) modelling. The purpose of this paper is to present an industrial and fully-automated optimal design tool, able to handle objectives, constraints, multi-parameters and multi-points optimization on a given CATIA CAD. The work is realized on Rapid And CostEffective Rotorcraft compound rotorcraft in the framework of the Fast RotorCraft Innovative Aircraft Demonstrator Platform (IADP) within the Clean Sky 2 programme.

The proposed solution relies on an automated CAD-CFD workflow called through the optimization process based on surrogate-based optimization (SBO) techniques. The SBO workflow has been specifically developed.

The methodology is validated on a simple configuration (bended pipe with two parameters). Then, the process is applied on a full compound rotorcraft to minimize the flow distortion at the engine entry. The design of the experiment and the optimization loop act on seven design parameters of the air inlet and for each individual the evaluation is performed on two operation points, namely, cruise flight and hover case. Finally, the best design is analyzed and aerodynamic performances are compared with the initial design.

The adding value of the developed process is to deal with geometric integration conflicts addressed through a specific CAD module and the implementation of a penalty function method to manage the unsuccessful evaluation of any individual.