752 publications from this institution
The pressure vessel design problem is a well-known design benchmark for validating bio-inspired optimization algorithms. However, its global optimality is not clear and there has been no mathematical proof put forward. In this paper, a detailed mathematical analysis of this problem is provided that proves that 6059.714335048436 is the global minimum. The Lagrange multiplier method is also used as an alternative proof and this method is extended to find the global optimum of a cantilever beam design problem.
No abstract is provided for this article.
Nowadays, many stochastic metaheuristics have been developed to solve various optimisation problems. The primary characteristics of these heuristics often involve the use of randomness in their search process. Essentially, randomness is useful when determining the next point in the search space and therefore has a crucial impact when exploring new solutions. In this paper, an extensive comparison is made between various probability distributions that can be used for randomising the swarm intelligence algorithms, e.g., uniform, Gaussian, Lévy flights, chaotic maps, and the random sampling in turbulent fractal cloud. These randomisation methods were incorporated into the bat algorithm that is one of the newest member of this domain. In line with this, various variants of bat algorithms randomised with different randomisation methods have been developed and extensive experiments were conducted on a well–known set of 24 BBOB benchmark functions. In addition, the results of randomised bat algorithms were compared with the results of the other well–known algorithms, including the firefly algorithm, differential evolution and artificial bee colony algorithms. The results of these experiments show that the efficiencies of the distributions used during the tests depend on the problem to be solved as well as on the algorithm used.
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.
The testability of equipment has become the key factor affecting equipment availability, and detracts from readiness and mission success. To overcome the current problems associated with the analysis of equipment testability, such as non-comprehensive failure mode coverage, low fault detection rate, and low fault location accuracy, this paper presents a system testability modeling and analysis method based on a summary of the results of device level failure mode effect and criticality analysis (FMECA), which is developed according to the failure data of components and a hardware impact analysis. In particular, we present a mathematical multi-signal model, quantitative methods and mathematical models of system testability, and the implementation processes of system testability modeling. The proposed method allows the failure modes of a module to be obtained accurately and comprehensively. By functioning at the device level, the method provides good fault location accuracy, and improves the authenticity of system testability analysis results. Finally, the testability of an actual electronic system is conducted using CARMES, which is a widely used reliability engineering software. The results verify the effectiveness and authenticity of the presented method, which can also provide a reference for the testability modeling and analysis of follow-up system design.
A mathematical model for poro-visco-plastic compaction and pressure solution in porous sediments has been formulated using the Voigt-type rheological constitutive relation as derived from experimental data. The governing equations reduce to a nonlinear hyperbolic heat conduction equation in the case of slow deformation where permeability is relatively high and the pore fluid pressure is nearly hydrostatic, while travelling wave exists in the opposite limit where over-pressuring occurs and the pore fluid pressure is almost quasi-lithostatic. Full numerical simulation using a finite element method agree well with the approximate analytical solutions.
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.
Keywords. Nonlinear systems; stability; control; adaptive control; motor control.
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.
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
No abstract is provided for this article.
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.
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.
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 calcium transport in biological systems is modelled as a reaction-diffusion process. Nonlinear calcium waves are then simulated using a stochastic cellular automaton whose rules are derived from the corresponding coupled partial differential equations. Numerical simulations show self-organized criticality in the complex calcium waves and patterns. Both the stochastic cellular automaton approach and the equation-based simulations can predict the characteristics of calcium waves and complex pattern formation. The implication of locality of calcium distribution with positional information in biological systems is also discussed.
