MSA for Optimal Reconfiguration and Capacitor Allocation in Radial/Ring Distribution Networks

This work presents a hybrid heuristic search algorithm called Moth Swarm Algorithm (MSA) in the context of power loss minimization of radial distribution networks (RDN) through optimal allocation and rating of shunt capacitors for enhancing the performance of distribution networks. With MSA, different optimization operators are used to mimic a set of behavioral patterns of moths in nature, which allows for flexible and powerful optimizer. Hence, a new dynamic selection strategy of crossover points is proposed based on population diversity to handle the difference vectors Lévy-mutation to force MSA jump out of stagnation and enhance its exploration ability. In addition, a spiral motion, adaptive Gaussian walks, and a novel associative learning mechanism with immediate memory are implemented to exploit the promising areas in the search space. In this article, the MSA is tested to adapt the objective function to reduce the system power losses, reduce total system cost and consequently increase the annual net saving with inequity constrains on capacitor size and voltage limits. The validation of the proposed algorithm has been tested and verified through small, medium and large scales of standard RDN of IEEE (33, 69, 85-bus) systems and also on ring main systems of 33 and 69-bus. In addition, the obtained results are compared with other algorithms to highlight the advantages of the proposed approach. Numerical results stated that the MSA can achieve optimal solutions for losses reduction and capacitor locations with finest performance compared with many existing algorithms

Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems

This paper presents a hybrid approach based on the Genetic Algorithm (GA) and Moth Swarm Algorithm (MSA), namely Genetic Moth Swarm Algorithm (GMSA), for determining the optimal location and sizing of renewable distributed generation (DG) sources on radial distribution networks (RDN). Minimizing the electrical power loss within the framework of system operation and under security constraints is the main objective of this study. In the proposed technique, the global search ability has been regulated by the incorporation of GA operations with adaptive mutation operator on the reconnaissance phase using genetic pathfinder moths. In addition, the selection of artificial light sources has been expanded over the swarm. The representation of individuals within the three phases of MSA has been modified in terms of quality and ratio. Elite individuals have been used to play different roles in order to reduce the design space and thus increase the exploitation ability. The developed GMSA has been applied on different scales of standard RDN of the (33 and 69-bus) power systems. Firstly, the most adequate buses for installing DGs are suggested using Voltage Stability Index (VSI). Then the proposed GMSA is applied to reduce real power generation, power loss, and total system cost, in addition, to improve the minimum bus voltage and the annual net saving by selecting the DGs size and their locations. Furthermore, GMSA is compared with other literature methods under several power system constraints and conditions, in single and multi-objective optimization space. The computational results prove the effectiveness and superiority of the GMSA with respect to power loss reduction and voltage profile enhancement using a minimum size of renewable DG units

Phase Retrieval and Design with Automatic Differentiation

The principal limitation in many areas of astronomy, especially for directly imaging exoplanets, arises from instability in the point spread function (PSF) delivered by the telescope and instrument. To understand the transfer function, it is often necessary to infer a set of optical aberrations given only the intensity distribution on the sensor - the problem of phase retrieval. This can be important for post-processing of existing data, or for the design of optical phase masks to engineer PSFs optimized to achieve high contrast, angular resolution, or astrometric stability. By exploiting newly efficient and flexible technology for automatic differentiation, which in recent years has undergone rapid development driven by machine learning, we can perform both phase retrieval and design in a way that is systematic, user-friendly, fast, and effective. By using modern gradient descent techniques, this converges efficiently and is easily extended to incorporate constraints and regularization. We illustrate the wide-ranging potential for this approach using our new package, Morphine. Challenging applications performed with this code include precise phase retrieval for both discrete and continuous phase distributions, even where information has been censored such as heavily-saturated sensor data. We also show that the same algorithms can optimize continuous or binary phase masks that are competitive with existing best solutions for two example problems: an Apodizing Phase Plate (APP) coronagraph for exoplanet direct imaging, and a diffractive pupil for narrow-angle astrometry. The Morphine source code and examples are available open-source, with a similar interface to the popular physical optics package Poppy

A small perturbation based optimization approach for the frequency placement of high aspect ratio wings

Design denotes the transformation of an identified need to its physical embodiment in a traditionally iterative approach of trial and error. Conceptual design plays a prominent role but an almost infinite number of possible solutions at the outset of design necessitates fast evaluations. The traditional practice of empirical databases loses adequacy for novel concepts and an ever increasing system complexity and resource scarsity mandate new approaches to adequately capture system characteristics. Contemporary concerns in atmospheric science and homeland security created an operational need for unconventional configurations. Unmanned long endurance flight at high altitudes offers a unique showcase for the exploration of new design spaces and the incidental deficit of conceptual modeling and simulation capabilities. The present research effort evolves around the development of an efficient and accurate optimization algorithm for high aspect ratio wings subject to natural frequency constraints. Foundational corner stones are beam dimensional reduction and modal perturbation redesign. Local and global analyses inherent to the former suggest corresponding levels of local and global optimization. The present approach departs from this suggestion. It introduces local level surrogate models to capacitate a methodology that consists of multi level analyses feeding into a single level optimization. The innovative heart of the new algorithm originates in small perturbation theory. A sequence of small perturbation solutions allows the optimizer to make incremental movements within the design space. It enables a directed search that is free of costly gradients. System matrices are decomposed based on a Timoshenko stiffness effect separation. The formulation of respective linear changes falls back on surrogate models that approximate cross sectional properties. Corresponding functional responses are readily available. Their direct use by the small perturbation based optimizer ensures constitutive laws and eliminates a previously necessary optimization at the local level. The great economy of the developed algorithm makes it suitable for the conceptual phase of aircraft design.Ph.D.Committee Chair: Mavris, Dimitri; Committee Member: Bauchau, Olivier; Committee Member: Schrage, Daniel; Committee Member: Volovoi, Vitali; Committee Member: Yu, Wenbi

Computational optimal control of the terminal bunt manoeuvre

This work focuses on a study of missile guidance in the form of trajectory shaping of a generic cruise missile attacking a fixed target which must be struck from above. The problem is reinterpreted using optimal control theory resulting in two formulations: I) minimum time-integrated altitude and 2) minimum flight time. Each formulation entails nonlinear, two-dimensional missile flight dynamics, boundary conditions and path constraints. Since the thus obtained optimal control problems do not admit analytical solutions, a recourse to computational optimal control is made. The focus here is on informed use of the tools of computational optimal control, rather than their development. Each of the formulations is solved using a three-stage approach. In stage I, the problem is discretised, effectively transforming it into a nonlinear programming problem, and hence suitable for approximate solution with the FORTRAN packages DIRCOL and NUDOCCCS. The results of this direct approach are used to discern the structure of the optimal solution, i.e. type of constraints active, time of their activation, switching and jump points. This qualitative analysis, employing the results of stage I and optimal control theory, constitutes stage 2. Finally, in stage 3, the insight of stage 2 are made precise by rigorous mathemati cal formulation of the relevant two-point boundary value problems (TPBVPs), using the appropriate theorems of optimal control theory. The TPBVPs obtained from this indirect approach are then solved using the FORTRAN package BNDSCO and the results compared with the appropriate solutions of stage I. For each formulation (minimum altitude and minimum time) the influence of boundary conditions on the structure of the optimal solution and the performance index is investigated. The results are then interpreted from the operational and computational perspectives. Software implementation employing DIRCOL, NUDOCCCS and BNDSCO, which produced the results, is described and documented. Finally, some conclusions are drawn and recommendations made

Multi-Scale Modeling, Surrogate-Based Analysis, and Optimization of Lithium-Ion Batteries for Vehicle Applications.

A common attribute of electric-powered aerospace vehicles and systems such as unmanned aerial vehicles, hybrid- and fully-electric aircraft, and satellites is that their performance is usually limited by the energy density of their batteries. Although lithium-ion batteries offer distinct advantages such as high voltage and low weight over other battery technologies, they are a relatively new development, and thus significant gaps in the understanding of the physical phenomena that govern battery performance remain. As a result of this limited understanding, batteries must often undergo a cumbersome design process involving many manual iterations based on rules of thumb and ad-hoc design principles. A systematic study of the relationship between operational, geometric, morphological, and material-dependent properties and performance metrics such as energy and power density is non-trivial due to the multiphysics, multiphase, and multiscale nature of the battery system. To address these challenges, two numerical frameworks are established in this dissertation: a process for analyzing and optimizing several key design variables using surrogate modeling tools and gradient-based optimizers, and a multi-scale model that incorporates more detailed microstructural information into the computationally efficient but limited macro-homogeneous model. In the surrogate modeling process, multi-dimensional maps for the cell energy density with respect to design variables such as the particle size, ion diffusivity, and electron conductivity of the porous cathode material are created. A combined surrogate- and gradient-based approach is employed to identify optimal values for cathode thickness and porosity under various operating conditions, and quantify the uncertainty in the surrogate model. The performance of multiple cathode materials is also compared by defining dimensionless transport parameters. The multi-scale model makes use of detailed 3-D FEM simulations conducted at the particle-level. A monodisperse system of ellipsoidal particles is used to simulate the effective transport coefficients and interfacial reaction current density within the porous microstructure. Microscopic simulation results are shown to match well with experimental measurements, while differing significantly from homogenization approximations used in the macroscopic model. Global sensitivity analysis and surrogate modeling tools are applied to couple the two length scales and complete the multi-scale model.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99980/1/wenbodu_1.pd

Algorithmic Advances to Increase the Fidelity Of Conceptual Hypersonic Mission Design

The contributions of this dissertation increase the fidelity of conceptual hypersonic mission design through the following innovations: 1) the introduction of coupling between the effects of ablation of the thermal protection system (TPS) and flight dynamics, 2) the introduction of rigid body dynamics into trajectory design, and 3) simplifying the design of hypersonic missions that involve multiple phases of flight. These contributions are combined into a unified conceptual mission design framework, which is in turn applicable to slender hypersonic vehicles with ablative TPS. Such vehicles are employed in military applications, wherein speed and terminal energy are of critical importance. The fundamental observation that results from these contributions is the substantial reduction in the maximum terminal energy that is achievable when compared to the state-of-the art conceptual design process. Additionally, the control history that is required to follow the maximum terminal energy trajectory is also significantly altered, which will in turn bear consequence on the design of the control actuators. The other important accomplishment of this dissertation is the demonstration of the ability to solve these class of problems using indirect methods. Despite being built on a strong foundation of the calculus of variations, the state-of-the-art entirely neglects indirect methods because of the challenge associated with solving the resulting boundary value problem (BVP) in a system of differential-algebraic equations (DAEs). Instead, it employs direct methods, wherein the optimality of the calculated trajectory is not guaranteed. The ability to employ indirect methods to solve for optimal trajectories that are comprised of multiple phases of flight while also accounting for the effects of ablation of the TPS and rigid body dynamics is a substantial advancement in the state-of-the-art

Problem decomposition by mutual information and force-based clustering

The scale of engineering problems has sharply increased over the last twenty years. Larger coupled systems, increasing complexity, and limited resources create a need for methods that automatically decompose problems into manageable sub-problems by discovering and leveraging problem structure. The ability to learn the coupling (inter-dependence) structure and reorganize the original problem could lead to large reductions in the time to analyze complex problems. Such decomposition methods could also provide engineering insight on the fundamental physics driving problem solution. This work forwards the current state of the art in engineering decomposition through the application of techniques originally developed within computer science and information theory. The work describes the current state of automatic problem decomposition in engineering and utilizes several promising ideas to advance the state of the practice. Mutual information is a novel metric for data dependence and works on both continuous and discrete data. Mutual information can measure both the linear and non-linear dependence between variables without the limitations of linear dependence measured through covariance. Mutual information is also able to handle data that does not have derivative information, unlike other metrics that require it. The value of mutual information to engineering design work is demonstrated on a planetary entry problem. This study utilizes a novel tool developed in this work for planetary entry system synthesis. A graphical method, force-based clustering, is used to discover related sub-graph structure as a function of problem structure and links ranked by their mutual information. This method does not require the stochastic use of neural networks and could be used with any link ranking method currently utilized in the field. Application of this method is demonstrated on a large, coupled low-thrust trajectory problem. Mutual information also serves as the basis for an alternative global optimizer, called MIMIC, which is unrelated to Genetic Algorithms. Advancement to the current practice demonstrates the use of MIMIC as a global method that explicitly models problem structure with mutual information, providing an alternate method for globally searching multi-modal domains. By leveraging discovered problem inter-dependencies, MIMIC may be appropriate for highly coupled problems or those with large function evaluation cost. This work introduces a useful addition to the MIMIC algorithm that enables its use on continuous input variables. By leveraging automatic decision tree generation methods from Machine Learning and a set of randomly generated test problems, decision trees for which method to apply are also created, quantifying decomposition performance over a large region of the design space.PhDCommittee Co-Chair: Braun, Robert D.; Committee Co-Chair: Clark, Ian G.; Committee Member: Chen, George T.; Committee Member: Clarke, John-Paul; Committee Member: Isbell, Charles L

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

Aerothermodynamic Optimization of Earth Entry Blunt Body Heat Shields for Lunar and Mars Return

A differential evolutionary algorithm has been executed to optimize the hypersonic aerodynamic and stagnation-point heat transfer performance of Earth entry heat shields for Lunar and Mars return manned missions with entry velocities of 11 and 12.5 km/s respectively. The aerothermodynamic performance of heat shield geometries with lift-to-drag ratios up to 1.0 is studied. Each considered heat shield geometry is composed of an axial profile tailored to fit a base cross section. Axial profiles consist of spherical segments, spherically blunted cones, and power laws. Heat shield cross sections include oblate and prolate ellipses, rounded-edge parallelograms, and blendings of the two. Aerothermodynamic models are based on modified Newtonian impact theory with semi-empirical correlations for convection and radiation. Multi-objective function optimization is performed to determine optimal trade-offs between performance parameters. Objective functions consist of minimizing heat load and heat flux and maximizing down range and cross range. Results indicate that skipping trajectories allow for vehicles with L/D = 0.3, 0.5, and 1.0 at lunar return flight conditions to produce maximum cross ranges of 950, 1500, and 3000 km respectively before Qs,tot increases dramatically. Maximum cross range increases by ~20% with an increase in entry velocity from 11 to 12.5 km/s. Optimal configurations for all three lift-to-drag ratios produce down ranges up to approximately 26,000 km for both lunar and Mars return. Assuming a 10,000 kg mass and L/D = 0.27, the current Orion configuration is projected to experience a heat load of approximately 68 kJ/cm2 for Mars return flight conditions. For both L/D = 0.3 and 0.5, a 30% increase in entry vehicle mass from 10,000 kg produces a 20-30% increase in Qs,tot. For a given L/D, highly-eccentric heat shields do not produce greater cross range or down range. With a 5 g deceleration limit and L/D = 0.3, a highly oblate cross section with an eccentricity of 0.968 produces a 35% reduction in heat load over designs with zero eccentricity due to the eccentric heat shield's greater drag area that allows the vehicle to decelerate higher in the atmosphere. In this case, the heat shield's drag area is traded off with volumetric efficiency while fulfilling the given set of mission requirements. Additionally, the high radius-of-curvature of the spherical segment axial profile provides the best combination of heat transfer and aerodynamic performance for both entry velocities and a 5 g deceleration limit