Swarm Intelligence

Swarm Intelligence has emerged as one of the most studied artificial intelligence branches during the last decade, constituting the fastest growing stream in the bio-inspired computation community. A clear trend can be deduced analyzing some of the most renowned scientific databases available, showing that the interest aroused by this branch has increased at a notable pace in the last years. This book describes the prominent theories and recent developments of Swarm Intelligence methods, and their application in all fields covered by engineering. This book unleashes a great opportunity for researchers, lecturers, and practitioners interested in Swarm Intelligence, optimization problems, and artificial intelligence

Artificial intelligence for superconducting transformers

Artificial intelligence (AI) techniques are currently widely used in different parts of the electrical engineering sector due to their privileges for being used in smarter manufacturing and accurate and efficient operating of electric devices. Power transformers are a vital and expensive asset in the power network, where their consistent and fault-free operation greatly impacts the reliability of the whole system. The superconducting transformer has the potential to fully modernize the power network in the near future with its invincible advantages, including much lighter weight, more compact size, much lower loss, and higher efficiency compared with conventional oil-immersed counterparts. In this article, we have looked into the perspective of using AI for revolutionizing superconducting transformer technology in many aspects related to their design, operation, condition monitoring, maintenance, and asset management. We believe that this article offers a roadmap for what could be and needs to be done in the current decade 2020-2030 to integrate AI into superconducting transformer technology

Spatio-Temporal Patterns act as Computational Mechanisms governing Emergent behavior in Robotic Swarms

open access articleOur goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their selfcoordinating emergent behavior, has proven ineffective, largely due to the swarm’s inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micromacro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm’s emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

The Application of ANN and ANFIS Prediction Models for Thermal Error Compensation on CNC Machine Tools

Thermal errors can have significant effects on Computer Numerical Control (CNC) machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This thesis first reviews different methods of designing thermal error models, before concentrating on employing Artificial Intelligence (AI) methods to design different thermal prediction models. In this research work the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as the backbone for thermal error modelling. The choice of inputs to the thermal model is a non-trivial decision which is ultimately a compromise between the ability to obtain data that sufficiently correlates with the thermal distortion and the cost of implementation of the necessary feedback sensors. In this thesis, temperature measurement was supplemented by direct distortion measurement at accessible locations. The location of temperature measurement must also provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results. In this thesis, a new intelligent system for reducing thermal errors of machine tools using data obtained from thermography data is introduced. Different groups of key temperature points on a machine can be identified from thermal images using a novel schema based on a Grey system theory and Fuzzy C-Means (FCM) clustering method. This novel method simplifies the modelling process, enhances the accuracy of the system and reduces the overall number of inputs to the model, since otherwise a much larger number of thermal sensors would be required to cover the entire structure. An Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means clustering (ANFIS-FCM) is then employed to design the thermal prediction model. In order to optimise the approach, a parametric study is carried out by changing the number of inputs and number of Membership Functions (MFs) to the ANFIS-FCM model, and comparing the relative robustness of the designs. The proposed approach has been validated on three different machine tools under different operation conditions. Thus the proposed system has been shown to be robust to different internal heat sources, ambient changes and is easily extensible to other CNC machine tools. Finally, the proposed method is shown to compare favourably against alternative approaches such as an Artificial Neural Network (ANN) model and different Grey models

Numerical Analysis and Modelling of Liquid Turbulence in Bubble Columns at Various Scales by Computational Fluid Dynamics

Diese Doktorarbeit beschäftigt sich mit der Entwicklung verbesserter statistischer Modelle für die Blasen-induzierte Turbulenz (Pseudo-Turbulenz). Es wird eine Skalen-übergreifende Herangehensweise gewählt, die sowohl Direkte Numerische Simulationen (DNS) als auch Euler Euler (E-E) Simulationen umfasst. Die dabei betrachteten Skalen umfassen Einzelblasen und Blasenschwärme sowie Blasensäulen im Labor- und Pilotmaßstab. Die Simulationsergebnisse werden jeweils anhand von Experimenten und Korrelationen verifiziert. Die Anwendbarkeit von Modellen für die ingenieurtechnische Berechnung von einem industriellen Blasensäulenreaktor auf Basis von numerischen Strömungssimulationen mit dem E-E Ansatz (Zwei-Fluid-Modell) wird nachgewiesen. Zur Modellentwicklung werden umfangreiche DNS Berechnungen für Blasen-schwärme durchgeführt. Hierfür wird das am KIT entwickelte Rechenprogramm TURBIT-VOF verwendet und ein Teilgebiet einer flachen Blasensäule betrachtet. Mittels der DNS-Daten wird die Transportgleichung der turbulenten kinetischen Energie (TKE) der Flüssigphase ($\textit{k}_{L}$) analysiert, die den Grundstein der ingenieurtechnischen Turbulenz-modellierung darstellt. Es zeigt sich, dass der dominierende Quellterm auf Grenzflächeneffekte zurückzuführen ist, während die Produktion aufgrund von Scherspannungen für die betrachteten Bedingungen gering ist. Produktions- und Dissipationsterm sind nicht im lokalen Gleichgewicht. Der Überschuss der Produktion von $\textit{k}_{L}$ in Bereichen mit hohem lokalem Gasgehalt wird durch Diffusion in Bereiche mit geringem Gasgehalt umverteilt. Für die zuverlässige Berechnung von Strömungen in Blasensäulen mit dem E-E Ansatz ist eine adäquate Modellierung des Grenzflächenterms in der $\textit{k}_{L}$-Gleichung daher von großer Bedeutung. Ansätze aus der Literatur zur Schließung dieses Terms werden durch Vergleich mit den DNS-Daten analysiert und zwei tragfähige Modelle ausgewählt. Mit dem $\textit{k}$-$\epsilon$ Zwei-Fluid-Modell in OpenFOAM® wird eine industrielle Blasensäule berechnet und der Einfluss des Grenzflächenterms in der $\textit{k}_{L}$-Gleichung untersucht. Das Turbulenzmodell hat bei Normaldruck nur einen sehr geringen, bei 18,5 bar Druck aber einen merklichen Einfluss auf den Gasgehalt und die Gas- und Flüssigkeitsgeschwindigkeit. Bei hohem Druck wird der gemessene Gasgehalt für ein Wassersystem in der Simulation deutlich überschätzt. Für ein organisches System liegen die numerischen und experimentellen Ergebnisse sehr nahe beieinander. Unter Verwendung des innerhalb dieser Arbeit implementierten Turbulenzmodells wird für das Wassersystem der Gasgehalt in Experimenten mit einer Abweichung von 9 – 13% berechnet. TKE-Profile werden für unterschiedliche Bedingungen analysiert und es wird eine lineare Beziehung zwischen TKE und lokalem Gasgehalt und mittlerer Gasgeschwindigkeit identifiziert. Die in dieser Arbeit verwendete skalenübergreifende Herangehensweise trägt zur verbesserten Turbulenzmodellierung in Blasenströmungen und damit zur Etablierung der numerischen Strömungssimulation für die Auslegung von industriellen Blasensäulen bei

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms