A review of optimization techniques in spacecraft flight trajectory design

For most atmospheric or exo-atmospheric spacecraft flight scenarios, a well-designed trajectory is usually a key for stable flight and for improved guidance and control of the vehicle. Although extensive research work has been carried out on the design of spacecraft trajectories for different mission profiles and many effective tools were successfully developed for optimizing the flight path, it is only in the recent five years that there has been a growing interest in planning the flight trajectories with the consideration of multiple mission objectives and various model errors/uncertainties. It is worth noting that in many practical spacecraft guidance, navigation and control systems, multiple performance indices and different types of uncertainties must frequently be considered during the path planning phase. As a result, these requirements bring the development of multi-objective spacecraft trajectory optimization methods as well as stochastic spacecraft trajectory optimization algorithms. This paper aims to broadly review the state-of-the-art development in numerical multi-objective trajectory optimization algorithms and stochastic trajectory planning techniques for spacecraft flight operations. A brief description of the mathematical formulation of the problem is firstly introduced. Following that, various optimization methods that can be effective for solving spacecraft trajectory planning problems are reviewed, including the gradient-based methods, the convexification-based methods, and the evolutionary/metaheuristic methods. The multi-objective spacecraft trajectory optimization formulation, together with different class of multi-objective optimization algorithms, is then overviewed. The key features such as the advantages and disadvantages of these recently-developed multi-objective techniques are summarised. Moreover, attentions are given to extend the original deterministic problem to a stochastic version. Some robust optimization strategies are also outlined to deal with the stochastic trajectory planning formulation. In addition, a special focus will be given on the recent applications of the optimized trajectory. Finally, some conclusions are drawn and future research on the development of multi-objective and stochastic trajectory optimization techniques is discussed

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

МЕТОД ВИБОРУ ОЗНАК ДЛЯ СИСТЕМИ ВИЯВЛЕННЯ ВТОРГНЕНЬ З ВИКОРИСТАННЯМ АНСАМБЛЕВОГО ПІДХОДУ ТА НЕЧІТКОЇ ЛОГІКИ

The study proposed a new method of constructing a set of important features for solving classification problems. This method is based on the idea of using an ensemble of estimators of the importance of features with summarization and the final result of the ensemble with the help of fuzzy logic algorithms. Statistical criteria (chi2, f_classif, correlation coefficient), mean decrease in impurity (MDI), mutual information criterion (mutual_info_classif) were used as estimators of the importance of features. Reducing the number of features on all data sets affects the accuracy of the assessment according to the criterion of the average reduction of classification errors. As long as the group of features in the data set for training contains the first features with the greatest influence, the accuracy of the model is at the initial level, but when at least one of the features with a large impact is excluded from the model, the accuracy of the model is noticeably reduced. The best classification results for all studied data sets were provided by classifiers based on trees or nearest neighbors: DesignTreeClassifier, ExtraTreeClassifier, KNeighborsClassifier. Due to the exclusion of non-essential features from the model, a noticeable increase in the speed of learning is achieved (up to 60-70%). Ensemble learning was used to increase the accuracy of the assessment. The VotingClassifier classifier, built on the basis of algorithms with the maximum learning speed, provided the best learning speed indicators. For future work, the goal is to further improve the proposed IDS model in the direction of improving the selection of classifiers to obtain optimal results, and setting the parameters of the selected classifiers, improving the strategy of generalizing the results of individual classifiers. For the proposed model, the ability to detect individual types of attacks with multi-class prediction is of significant interest.У дослідженні був запропонований новий метод побудови набору важливих ознак для вирішення задач класифікації. Цей метод заснований на ідеє використання ансамбля оцінювачів важливості ознак з підведенням підсумків і кінцевого результату ансамбля за допомо-гою алгоритмів нечіткої логіки. В якості оцінювачів важливості ознак було використано статистичні критерії (chi2, f_classif, коефіцієнт кореляції), критерій середнього зменшення помилок класифікації (mean decrease in impurity - MDI), критерій взаємної інформації (mutual_info_classif). Зменшення кількості ознак на усіх наборах даних впливає на точність оцінювання відповідно до критерію середнього зменшення помилок класифікації. Поки група ознак в на-борі даних для навчання містить перши за списком ознаки з найбільшим впливом, точність моделі знаходиться на початковому рівні, але при виключенні з моделі хоча б однієї з ознак з великим впливом, точність моделі помітно знижується. Найкращі результати класифікації для усіх досліджених наборів даних забезпечили класифікатори на основі дерев або найближчих сусідів: DecignTreeClassifier, ExtraTreeClassifier, KNeighborsClassifier. За рахунок виключення із моделі несуттєвих ознак досягається помітне збільшення швидкості навчання (до 60-70%). Для підвищення точності оцінювання було використано ансамблеве навчання. Найкращі показники за швидкістю навчання забезпечив класифікатор VotingClassifier, побудований на базі алгоритмів з максимальною швидкістю навчання. Для майбутньої роботи метою є подальше вдосконалення запропонованої моделі IDS в напрямках вдосконалення вибору класифікаторів для отримання оптимальних результатів, та налаштування параметрів вибраних класифікаторів, удосконалення стратегії узагальнення результатів окремих класифікаторів. Для запропонованої моделі істотний інтерес представляє можливість виявлення окремих типів атак з урахуванням багатокласового прогнозування

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Development of an Overset Structured 2D RANS/URANS Navier-Stokes Solver Using an Implicit Space and Non-Linear Frequency Domain Time Operators

RÉSUMÉ Ce projet s’intéresse au développement de méthodes avancées afin de réaliser des simulations numériques en mécanique des fluides. Plus particulièrement, ces méthodes s’appliqueront aux modèles RANS et URANS sur des profils aérodynamiques simples et multiéléments. Ces développements seront utilisés afin de réaliser une optimisation sur l’interstice et le chevauchement d’un volet de bord de fuite ainsi que des simulations instationnaires standard. Le logiciel utilisé comme plateforme de développement est NSCODE, un solveur Navier-Stokes bidimensionnel pour maillages structurés. Les développements logiciels seront réalisés dans un cadre rigoureux et en utilisant des techniques de programmations appropriées. Les méthodes implémentées viseront plusieurs aspects du solveur incluant les capacités topologiques, l’opérateur spatial et l’opérateur temporel. Afin de traiter les profils multi-éléments, la méthode multi-blocs sera implémentée afin de partitionner le domaine de calcul. La méthode chimère est ensuite implémentée afin d’améliorer la flexibilité de la méthode multi-blocs. Le schéma de dissipation artificielle scalaire est ensuite remplacé par le schéma de dissipation matricielle afin d’améliorer la précision du solveur. Un schéma d’opérateur spatial utilisant un préconditionneur Jacobien implicite par point ainsi qu’un schéma implicite Block Lower-Upper Symmetric Gauss Seidel (LU-SGS) sont ensuite implémentés afin d’améliorer le taux de convergence du solveur. L’opérateur temporel par pas de temps double présent dans le logiciel initial est adapté afin d’être compatible avec les différents schémas d’opérateurs spatiaux utilisés. Un opérateur temporel Non-Linéaire dans le Domaine Fréquentiel (NLFD) est ensuite implémenté afin de résoudre efficacement les écoulements instationnaires périodiques. Chacune des méthodes implémentées est validée et vérifiée en utilisant des cas tests utilisés dans la littérature ainsi qu’avec des résultats expérimentaux. Une grande variété de cas tests sont utilisés afin de s’assurer de la fiabilité du solveur lors des applications futures. Les implémentations logicielles sont ensuite utilisés afin de résoudre deux problèmes: • Une optimisation de dispositif hypersustentateur; • Une simulation dans le domaine fréquentiel d’un profil aérodynamique en tangage dans un écoulement turbulent. L’optimisation du dispositif hypersustentateur vise à maximiser le coefficient de portance du profil de recherche MDA en modifiant la position du volet de bord de fuite. En utilisant une optimisation utilisant des simulations purement bidimensionnelles en parallèle à une optimisation utilisant des simulations tridimensionnelles utilisant l’hypothèse d’aile en flèche infinie, la démonstration est faite qu’une optimisation bidimensionnelle n’est pas adaptée au design de dispositifs hypersustentateurs sur des ailes en flèches. La seconde application a pour but d’introduire un modèle de turbulence aux simulations NLFD dans NSCODE. En tant qu’étape vers l’utilisation de modèles de turbulence à une et deux équations, un modèle algébrique est utilisé. Ce problème vérifiera donc l’utilisation d’un modèle de turbulence algébrique sur un opérateur NLFD. La simulation NLFD d’un profil en tangage dans un écoulement turbulent donne des résultats en accord avec la littérature et avec les simulations par pas de temps double ce qui ouvre la voie à l’utilisation de modèles de turbulence plus complexes avec la méthode NLFD.----------ABSTRACT This project aims at performing 2D RANS and URANS computational fluid dynamics simulations over single and multi-element airfoil. The application of such developments is demonstrated via flap gap/overlap optimisation and standard URANS cases. The work presented in this thesis was implemented in NSCODE, a 2D structured grid Reynolds-Averaged Navier-Stokes flow solver, and is included in a solid framework to ensure its quality and maintainability. It covers many aspects of the flow solver, including topology capabilities, steady and unsteady solver schemes. To simulate flows around complex geometries, the multi block technique is implemented in order to partition the computational domain. The multi block capability is then expanded to overset meshes with the Chimera method to allow for even more flexibility in geometry treatment. The existing scalar dissipation scheme is replaced by the matricial artificial dissipation scheme (MATD) to increase spatial resolution accuracy. A point implicit Point-Jacobi Preconditioner and an implicit Block Lower-Upper Symmetric Gauss Seidel (LU-SGS) space solving scheme are then implemented to increase convergence rates. The time discretization schemes are also improved. The baseline dual time stepping scheme is modified to be compatible with the LU-SGS solver schemes. A Non-Linear Frequency Domain is also added to the software in order to efficiently solve periodic problems. Each of these techniques is validated and verified against literature data and experimental data. A wide range of test case is chosen in order to ensure full confidence in the developed software. The software capability developments are then used to solve two problems: • A high-lift airfoil optimisation; • An unsteady simulation of turbulent flows in the frequency domain of a pitching airfoil. The high-lift airfoil optimisation seeks to maximise the lift coefficient of the McDonnel Douglas Research airfoil by changing the flap’s position. Using a two dimensional approach in parallel to a three dimensional approach with infinite swept wing hypothesis, a physical phenomenon that couldn’t be previously observed on two dimensional solvers was captured. The second case sought to introduce a turbulence component to the NLFD implementation in NSCODE. As a step before using one and two equations turbulence models, an algebraic turbulence model is used in the study. This problem will thus test the applicability of an vii algebraic turbulence model to the NLFD method. The NLFD resolution of a turbulent pitching airfoil yielded results that validated very well with the literature and equivalent Dual Time Stepping resolutions, paving the way for the use of more complex turbulence models in NLFD resolutions

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

Development, evolution and genetic analysis of C. elegans-inspired foraging algorithms under different environmental conditions

In this work 3 minimalist bio-inspired foraging algorithms based on C. elegans’ chemotaxis and foraging behaviour were developed and investigated. The main goal of the work is to apply the algorithms to robots with limited sensing capabilities. The refined versions of these algorithms were developed and optimised in 22 different environments. The results were processed using a novel set of techniques presented here, named Genotype Clustering. The results lead to two distinct conclusions, one practical and one more academic. From a practical perspective, the results suggest that, when suitably tuned, minimalist C. elegans-inspired foraging algorithms can lead to effective navigation to unknown targets even in the presence of repellents and under the influence of a significant sensor noise. From an academic perspective, the work demonstrates that even simple models can serve as an interesting and informative testbed for exploring fundamental evolutionary principles. The simulated robots were grounded in real hardware parameters, aiming at future application of the foraging algorithms in real robots. Another achievement of the project was the development of the simulation framework that provides a simple yet flexible program for the development and optimisation of behavioural algorithms

Supermanoeuvrability in a biomimetic morphing-wing aircraft

In this work we study the supermanoeuvrability of a biomimetic morphing-wing case study aircraft system. Analytical and computational models of biomimetic flight dynamics are developed, utilising multibody dynamics, computational fluid dynamics, and reduced-order aerodynamic models; and validated with respect to experimentally-derived flight dynamics of a Pioneer RQ-2 UAV. These models are used to explore the capability of this system for a wide range of biological and other supermanoeuvres: multi-axis quasistatic nose-pointing-and-shooting (NPAS) / direct force capability; multi-axis rapid-nose-pointing-and-shooting (RaNPAS) including Pugachev’s cobra; ballistic transition; and anchor turning. Novel contributions include the development of transient aerodynamic models for a three-dimensional flight-simulation context; the development of novel methods for assessing transient model validity; the development of improved methods of quaternion variational integration; the development of quasi-trim and continuation-based methods for the design, exploration, analysis and control of manoeuvres in biomimetic morphing-wing systems; an assessment of the complex spiral mode stability effects present in asymmetrically-morphed system trim states; and a demonstration of the wide-ranging potential for advanced supermanoeuvrability in biomimetic morphing-wing systems. Industrial applications include the design of high-precision guided missiles for use in complex, e.g. urban, environments.Cambridge Commonwealth Prince of Wales Scholarship (Cambridge Trust

Data-driven solutions to enhance planning, operation and design tools in Industry 4.0 context

This thesis proposes three different data-driven solutions to be combined to state-of-the-art solvers and tools in order to primarily enhance their computational performances. The problem of efficiently designing the open sea floating platforms on which wind turbines can be mount on will be tackled, as well as the tuning of a data-driven engine's monitoring tool for maritime transportation. Finally, the activities of SAT and ASP solvers will be thoroughly studied and a deep learning architecture will be proposed to enhance the heuristics-based solving approach adopted by such software. The covered domains are different and the same is true for their respective targets. Nonetheless, the proposed Artificial Intelligence and Machine Learning algorithms are shared as well as the overall picture: promote Industrial AI and meet the constraints imposed by Industry 4.0 vision. The lesser presence of human-in-the-loop, a data-driven approach to discover causalities otherwise ignored, a special attention to the environmental impact of industries' emissions, a real and efficient exploitation of the Big Data available today are just a subset of the latter. Hence, from a broader perspective, the experiments carried out within this thesis are driven towards the aforementioned targets and the resulting outcomes are satisfactory enough to potentially convince the research community and industrialists that they are not just "visions" but they can be actually put into practice. However, it is still an introduction to the topic and the developed models are at what can be defined a "pilot" stage. Nonetheless, the results are promising and they pave the way towards further improvements and the consolidation of the dictates of Industry 4.0