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Modelling Manufacturing Systems with Place/Transition Nets and Timed Petri Nets
Published in Javier Campos, Carla Seatzu, Xiaolan Xie, in Manufacturing, 2018
Maria Paola Cabasino, Mariagrazia Dotoli, Carla Seatzu
The integration of discrete and continuous PNs has led to hybrid PNs (HPNs), which are especially useful to model hybrid dynamical systems, that is, systems comprising a discrete and a continuous part. In other words, the fluidification of the system model is partial, so that some components of the system state still evolve according to the occurrence of discrete events, while others evolve as a continuous function of time. In the case of manufacturing systems, HPNs are suited to model systems in which only a fraction of the parts moving in the system is approximated by a continuous flow due to the high population of the corresponding subsystem of the manufacturing system. Examples of manufacturing systems modelled by HPNs may be found in [9,32]. A particular subclass of HPNs useful for modelling manufacturing systems is the first-order HPN (FOHPN) formalism proposed in [1]. This HPN model is a straightforward and formal tool that allows modelling and control manufacturing systems, thanks to its ability to represent the hybrid system dynamics using a linear discrete-time time-varying state variable model, which can be effectively implemented for simulation and performance evaluation purposes in computational engineering environments. Several examples using the FOHPN framework for modelling manufacturing systems are proposed in [2]. Moreover, several application examples in the use of this framework for distributed manufacturing systems modelling and control may be found in [16,17].
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Published in Splinter Robert, Illustrated Encyclopedia of Applied and Engineering Physics, 2017
[computational, engineering, solid-state] Two different meanings are associated with annealing, one for the solid-state engineering application and the other in computational science. In material properties, annealing references a heat treatment. The surface may be chemically altered to make it more resistant to scratches or thermally modified to make the material resilient to applied force in order to induce geometric modifications. Rapid heating and slow cooling will reduce the mechanical strength, whereas regular heating and fast cooling can increase the mechanical strength, as used to give hardness to the blade of a sword. In computational sciences, annealing of data refers to the process of reducing the solution domain in order to limit the complexity of a system, making it unsolvable. In quantum mechanics, the quantum annealing technique provides the perturbation mechanism of action for combinatorial optimization of a ground state problem for systems with a crystal structure. The crystalline, or glassy dynamics, refers to developing process at extreme slow incremental evolution, also referred to as “relaxation.” The relaxed state will have amorphous quantum states on macroscopic scale. In quantum mechanics, the quantum annealing provides the tools to solve for the time-dependent Schrödinger equation in a real-time process within a range of constraints and approximations.
Fuzzy Logic
Published in Paresh Chra Deka, A Primer on Machine Learning Applications in Civil Engineering, 2019
In many fields of science (physics, engineering, economics, social, political sciences, etc.) and disciplines, where fuzzy sets and fuzzy logic are applied (e.g., approximate reasoning, image processing, fuzzy systems modeling and control, fuzzy decision-making, statistics, operations research and optimization, computational engineering, artificial intelligence, and fuzzy finance and business) fuzzy numbers and arithmetic play a central role and are frequently, and increasingly, the main instruments. Many studies are reported in this area such as, Kaufmann and Gupta, 1985 and Kim and Mendel, 1995.
Preface to the Special Issue on: “27th International Conference on Parallel Computational Fluid Dynamics”
Published in International Journal of Computational Fluid Dynamics, 2016
The international Conference on Parallel Computational Fluid Dynamics (CFD) is an annual event devoted to the discussion of the latest advances and challenges in the field of high-performance computing for fluid dynamics problems and related multidisciplinary applications. The 27th edition of the conference took place in May 2015 in Montreal, Canada, and was hosted by the CFD Laboratory of the McGill University. The Conference had seven plenary speakers, three parallel sessions and two mini symposia, entitled ‘Enabling Large-Scale Multi-physics Simulations’ and ‘CFD Applications on GPU and Many-Core Architecture’, respectively. A total of 88 research works were presented during the Conference, for a total of 110 participants from both Academia and Industry. Two-thirds of the presentations discussed themes in the area of parallel algorithms and software, GPU computing and peta-scale applications. The remaining one-third addressed the application of parallel solvers to problems in the areas of mechanical and biomedical engineering, turbulent flows, combustion and multi-physics problems. This Special Issue collects a selection of the works presented at the conference. The conference was successful in delivering high-quality talks in terms of scientific relevance and in proposing approaches to address the most modern and (possibly) future demands in the area of high-performance computing and computational engineering.
Analysis and design of systems driven by finite-time convergent controllers: practical stability approach
Published in International Journal of Control, 2018
Antonio Rosales, Yuri Shtessel, Leonid Fridman
The definition of practical stability margins that characterise the robustness of single-input–single-output systems driven by nonlinear finite-time convergent controllers to cascade unmodelled dynamics was presented. Stability margins were obtained in terms of phase and gain margins. The identification algorithms of the practical phase and gain margins are presented using the DF-HB-based algorithm, which is an approximated computational/engineering tool. Linear dynamic compensator design in cascade with finite-time convergent controller is proposed in order to guarantee desired practical stability margins, if necessary. The proposed techniques and algorithms are verified and illustrated via numerous examples and simulations.
Using a hybrid artificial intelligence approach to estimate length of the hydraulic jump caused by novel kind of Sharp-Crested V-notch weirs
Published in European Journal of Environmental and Civil Engineering, 2022
Over the last decades, optimization algorithms were selected as an expert scheme to improve the performance and dilemmas relevant to classical standalone DDM in modeling systems related to engineering problems (Elbaz et al., 2020; Lin et al., 2021; Zhang et al., 2020a, 2020b, 2020c, 2021). These algorithms facilitate automatically estimation factors of the single DDM. The ant colony optimization algorithm ACO is a probabilistic strategy for solving computational engineering problems.