The project management system is briefly introduced and the development of mill supervision and inspection for oil and gas pipe line project is summarized in this articles.It also discusses how to use scientific standards of project management system to implement supervision and inspection projects,as well as introduces how to manage the initiating,planning,executing,monitoring,closing and change for the projects,which provide the basis and reference for mill supervision and inspection of oil and gas pipe line engineering project.
Keywords. Nonlinear systems; stability; control; adaptive control; motor control.
A new metaheuristic optimisation algorithm, called Cuckoo Search (CS), was developed recently by Yang and Deb (2009). This paper presents a more extensive comparison study using some standard test functions and newly designed stochastic test functions. We then apply the CS algorithm to solve engineering design optimisation problems, including the design of springs and welded beam structures. The optimal solutions obtained by CS are far better than the best solutions obtained by an efficient particle swarm optimiser. We will discuss the unique search features used in CS and the implications for further research.
Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate.
Purpose – The purpose of this paper is to study the slime mould Physarum polycephalum Design/methodology/approach – The paper proceeds by representing major urban areas of China by oat flakes, inoculating the slime mould in Beijing, waiting till the slime mould colonises all urban areas, or colonises some and cease further propagation, and analysing the protoplasmic networks formed and comparing with man-made motorway network and planar proximity graphs. Findings Findings – Laboratory experiments found that P. polycephalum Originality/value – The paper demonstrated the strong component of transport system built by slime mould of P. polycephalum
State-of-the-art review of cellular automata, cellular automata for partial differential equations, differential equations for cellular automata and pattern formation in biology and engineering.
Swarm intelligence and bio-inspired algorithms form a hot topic in the developments of new algorithms inspired by nature. These nature-inspired metaheuristic algorithms can be based on swarm intelligence, biological systems, physical and chemical systems. Therefore, these algorithms can be called swarm-intelligence-based, bio-inspired, physics-based and chemistry-based, depending on the sources of inspiration. Though not all of them are efficient, a few algorithms have proved to be very effi cient and thus have become popular tools for solving real-world problems. Some algorithms are insuffici ently studied. The purpose of this review is to present a relatively comprehensive list of all the algorithms in the literature, so as to inspire further research.
No abstract is provided for this article.
For rapid and cost-effective hammer drilling, accurate prediction of rock impact response is crucial for designing optimal bits and maximising rock fragmentation. Current design optimisation workflows combine numerical simulations and experiments but often require numerous iterations to pinpoint the optimal design. Although physics-based models can potentially reduce experimental expenses, their significant computational demands present challenges when simulating the complex fragmentation dynamics during drill bit-rock interactions. This study introduces a data-driven artificial intelligence (AI) model, employing a multilayer perceptron (MLP) as a surrogate. The model leverages the hybrid finite-discrete element model (FDEM) as a powerful method in rock fracture mechanics to generate a sufficiently large training dataset. An automated workflow has been developed for generating the training data, comprising a pipeline that includes pre-processing, solving, and post-processing modules. Subsequently, the AI models were integrated into an optimisation framework alongside uncertainty quantification to demonstrate their potential in enhancing drilling efficiency through optimised bit design and operations. The MLP exhibits high accuracy in predicting key parameters, including rebound velocity, total crack length, quantities of fragments with different sizes and maximum contact force between rock and insert. Notably, this approach achieves real-time prediction compared to the 5-7 min simulation times of FDEM. Integrating this data-driven model into a design framework enables rapid assessment of different bit designs under various operational conditions. More broadly, this approach has the potential to impact other applications, such as digital twins, serving as a forward and inverse model for predicting rock type and optimising drilling performance.
The project management system is briefly introduced and the development of mill supervision and inspection for oil and gas pipe line project is summarized in this articles.It also discusses how to use scientific standards of project management system to implement supervision and inspection projects,as well as introduces how to manage the initiating,planning,executing,monitoring,closing and change for the projects,which provide the basis and reference for mill supervision and inspection of oil and gas pipe line engineering project.
Compaction in reactive porous media is modelled as a reaction-diffusion process with a moving boundary. Asymptotic analysis is used to find solutions for the coupled nonlinear compaction equations, and a traveling wave solution is obtained above the reaction zone.
Many physical quantities such as stress and strain are tensors. Vectors are essentially first-order tensors. Tensors are the extension of vectors, and they can have any number of dimensions and any orders, though most commonly used tensors are second-order tensors. This chapter introduces the basic concepts of tensors and tensor algebra.
Granular flow, particle mixing and powder compaction are very important processes in many industrial applications such as pharmaceutical tabletting, minerals selection, casting, packing and transport of granular materials. Flow and deformation of particles has been typically treated as a continuum process, and thus the Finite Element Method (FEM) has been widely used. In reality, however, granular powder consists of millions of tiny particles of different sizes and shapes, the conventional continuum method cannot model these discrete features. Naturally, a discrete element method can provide more realistic results using uniform spherical particles. Furthermore, powder flow and deformation modelling involve rheology, particle size distribution, fracture, and mixture of different materials. The modelling of such processes is very difficulty due to their nonlinearity. Thus, the proper modelling of powder flow and compaction is of both industrial importance and scientific interest. The main issue in discrete element modelling is the contact detection of interacting particles during the flow and deformation processes. In fact, nearly two-thirds of the computing time is used for the contact detection purpose. Thus, the efficiency of the contact detection algorithms used in the discrete element analysis is crucially important to the whole modelling procedure. A number of good contact detection algorithms have been developed such as the direct check algorithm, binary tree algorithm, contact list and grid searching algorithms. However, most of these algorithms work well for rigid spherical or triangular particles. They generally do not apply well to the non-spherical particles, or at least there is no guarantee of their efficiency. So many existing studies have focused on the flow and transport of rigid spherical particles and soft particles. Recently, there have been new developments concerning flow of non-spherical particles such as elliptical shapes and/or triangular particles. In this paper, we present a two-stage contact detection algorithm for non-spherical particles using the contact list algorithm and direct polygon contact detection. This method combines the advantages of several existing contact detection algorithms so as to find an efficient way to deal with the interactions of non-spherical particles, and simply treating the spherical particle as a degenerated case of non-spherical shapes. In this way, we can essentially deal with any particle shape. In the next sections, we will give a brief description of the two-stage contact detection algorithm, and then provide some simulation results and compare the efficiency with other contact detection procedures.
Nature-inspired algorithms have attracted much attention in recent years, and there are more than 40 different algorithms with more than 100 variants. This chapter briefly outlines a few other classes of optimization algorithms such as harmony search. Hybrid algorithms are also discussed and further research topics are highlighted.
Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Levy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Levy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.
