Multiobjective Topology Optimization for Preliminary Design Using Graph Theory and L-System Languages

Topology optimization is a powerful tool that, when employed at the preliminary stage of the design process, can determine potential structural configurations that best satisfy specified performance objectives. However, the use of conventional topology optimization approaches such as density-based and level set methods requires a fair amount of user knowledge of or intuition for both the design problem being considered and the desired result. While straightforward for simple structural problems with a relatively small design space, advancements in the area of smart materials and a growing interest in developing structures with increased multifunctionality may begin to render these methods as ineffective. Thus, there is a growing need for an inherently multiobjective preliminary design tool capable of exploring a vast design space to identify well-performing solutions to problems with which users have little/no intuition or experience. This work proposes the use of a heuristic alternative to conventional topology optimization approaches which couples a genetic algorithm with a parallel rewriting system known as a Lindenmayer System (L-System). The L-System encodes design variables into a string of characters that, when interpreted by a deterministic algorithm, governs the development of the topology. In particular, this work explores two distinct L-System interpretation approaches. The first is a geometry-based approach known as turtle graphics, which tracks its spatial position and orientation at all times and constructs line segments between specified coordinates. The second is a newly-developed graph-based approach referred to as Spatial Interpretation for the Development of Reconfigurable Structures (SPIDRS). This algorithm is based on the nodes, edges, and faces of a planar graph, allowing for an edge- and face-constructing agent to move more freely around the design space and introduce deliberate and natural topological modifications. This graph-based approach can also be extended to consider a three-dimensional structural design domain, the first known demonstration of 3-D L-System topology optimization. It will be demonstrated that the proposed L-System topology optimization framework effectively explores the physical design space and results in configurations comparable to both known optimal or ideal solutions as well as those found using conventional topology optimization methods, but with the advantage of straightforward multiobjective/multiphysical extension. The implementation of a sizing optimization scheme to determine optimal structural member thicknesses for SPIDRS-generated topologies will also be discussed, and several potential multiphysical design applications will be introduced

Topology optimization of the line-start synchronous machines

Diplomová práce se zabývá topologickou optimalizací elektrických strojů a reluktančními synchronními stroji spouštěnými za sítě. Práce obsahuje literární rešerši na téma topologické optimalizace elektrických strojů a na téma synchronní reluktanční stroj spouštěný ze sítě. Jsou zde popsány možné způsoby charakterizace optimalizovaného prostoru. Především je rozebrán vliv rozmístění Gaussových funkcí na finální Gaussovu síť. V této práci je vytvořen vyhodnocovací algoritmus pro jednotlivé jedince, který zajišťuje komunikaci mezi Ansys Maxwell a optimalizačním softwarem SyMSpace. Navíc tento algoritmus vede ke zkrácení výpočetní doby počáteční selekcí nevyhovujících jedinců. Dále je provedena topologická optimalizace LSSynRM s využitím normalizované Gaussovy sítě a zhodnocení výsledků.The master thesis deals with topology optimization of electrical machines and line-start synchronous reluctance motor. The master thesis includes a literature review on the state of the art of topology optimizations and line-start synchronous reluctance motor. The possible concepts for characterizing the investigated design space is described. The dependency of the final normalized Gaussian network on the distribution of Gaussian functions is analysed in detail. The evaluation algorithm of a single individual is created within this thesis. The algorithm manages communication between Ansys Maxwell and software tool SyMSpace. Moreover, the algorithm leads to reducing of the computational time due to the preselection of unfeasible geometries. Furthermore, the topology optimization of LSSynRM based on the normalized Gaussian network is performed and the results are discussed.

Non-weighted aggregate evaluation function of multi-objective optimization for knock engine modeling

In decision theory, the weighted sum model (WSM) is the best known Multi-Criteria Decision Analysis (MCDA) approach for evaluating a number of alternatives in terms of a number of decision criteria. Assigning weights is a difficult task, especially if the number of criteria is large and the criteria are very different in character. There are some problems in the real world which utilize conflicting criteria and mutual effect. In the field of automotive, the knocking phenomenon in internal combustion or spark ignition engines limits the efficiency of the engine. Power and fuel economy can be maximized by optimizing some factors that affect the knocking phenomenon, such as temperature, throttle position sensor, spark ignition timing, and revolution per minute. Detecting knocks and controlling the above factors or criteria may allow the engine to run at the best power and fuel economy. The best decision must arise from selecting the optimum trade-off within the above criteria. The main objective of this study was to proposed a new Non-Weighted Aggregate Evaluation Function (NWAEF) model for non-linear multi-objectives function which will simulate the engine knock behavior (non-linear dependent variable) in order to optimize non-linear decision factors (non-linear independent variables). This study has focused on the construction of a NWAEF model by using a curve fitting technique and partial derivatives. It also aims to optimize the nonlinear nature of the factors by using Genetic Algorithm (GA) as well as investigate the behavior of such function. This study assumes that a partial and mutual influence between factors is required before such factors can be optimized. The Akaike Information Criterion (AIC) is used to balance the complexity of the model and the data loss, which can help assess the range of the tested models and choose the best ones. Some statistical tools are also used in this thesis to assess and identify the most powerful explanation in the model. The first derivative is used to simplify the form of evaluation function. The NWAEF model was compared to Random Weights Genetic Algorithm (RWGA) model by using five data sets taken from different internal combustion engines. There was a relatively large variation in elapsed time to get to the best solution between the two model. Experimental results in application aspect (Internal combustion engines) show that the new model participates in decreasing the elapsed time. This research provides a form of knock control within the subspace that can enhance the efficiency and performance of the engine, improve fuel economy, and reduce regulated emissions and pollution. Combined with new concepts in the engine design, this model can be used for improving the control strategies and providing accurate information to the Engine Control Unit (ECU), which will control the knock faster and ensure the perfect condition of the engine

On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

An information driven hybrid evolutionary algorithm for optimal design of a Net Zero Energy House

Building Performance Simulation (BPS) is a powerful tool to estimate and reduce building energy consumption at the design stage. However, the true potential of BPS remains unrealized if trial and error simulation methods are practiced to identify combinations of parameters to reduce energy use of design alternatives. Optimization algorithms coupled with BPS is a process-orientated tool which identifies optimal building configurations using conflicting performance indicators. However, the application of optimization approaches to building design is not common practice due to time and computation requirements. This paper proposes a hybrid evolutionary algorithm which uses information gained during previous simulations to expedite and improve algorithm convergence using targeted deterministic searches. This technique is applied to a net-zero energy home case study to optimize trade-offs in passive solar gains and active solar generation using a cost constraint

Learning From Geometry In Learning For Tactical And Strategic Decision Domains

Artificial neural networks (ANNs) are an abstraction of the low-level architecture of biological brains that are often applied in general problem solving and function approximation. Neuroevolution (NE), i.e. the evolution of ANNs, has proven effective at solving problems in a variety of domains. Information from the domain is input to the ANN, which outputs its desired actions. This dissertation presents a new NE algorithm called Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT), based on a novel indirect encoding of ANNs. The key insight in HyperNEAT is to make the algorithm aware of the geometry in which the ANNs are embedded and thereby exploit such domain geometry to evolve ANNs more effectively. The dissertation focuses on applying HyperNEAT to tactical and strategic decision domains. These domains involve simultaneously considering short-term tactics while also balancing long-term strategies. Board games such as checkers and Go are canonical examples of such domains; however, they also include real-time strategy games and military scenarios. The dissertation details three proposed extensions to HyperNEAT designed to work in tactical and strategic decision domains. The first is an action selector ANN architecture that allows the ANN to indicate its judgements on every possible action all at once. The second technique is called substrate extrapolation. It allows learning basic concepts at a low resolution, and then increasing the resolution to learn more advanced concepts. The iii final extension is geometric game-tree pruning, whereby HyperNEAT can endow the ANN the ability to focus on specific areas of a domain (such as a checkers board) that deserve more inspection. The culminating contribution is to demonstrate the ability of HyperNEAT with these extensions to play Go, a most challenging game for artificial intelligence, by combining HyperNEAT with UC

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Refined Genetic Algorithms for Polypeptide Structure Prediction

Accurate and reliable prediction of macromolecular structures has eluded researchers for nearly 40 years. Prediction via energy minimization assumes the native conformation has the globally minimal energy potential. An exhaustive search is impossible since for molecules of normal size, the size of the search space exceeds the size of the universe. Domain knowledge sources, such as the Brookhaven PDB can be mined for constraints to limit the search space. Genetic algorithms (GAs) are stochastic, population based, search algorithms of polynomial (P) time complexity that can produce semi-optimal solutions for problems of nondeterministic polynomial (NP) time complexity such as PSP. Three refined GAs are presented: A farming model parallel hybrid GA (PHGA) preserves the effectiveness of the serial algorithm with substantial speed up. Portability across distributed and MPP platforms is accomplished with the Message Passing Interface (MPI) communications standard. A Real-valved GA system, real-valued Genetic Algorithm, Limited by constraints (REGAL), exploiting domain knowledge. Experiments with the pentapeptide Met-enkephalin have identified conformers with lower energies (CHARMM) than the accepted optimal conformer (Scheraga, et al), -31.98 vs -28.96 kcals/mol. Analysis of exogenous parameters yields additional insight into performance. A parallel version (Para-REGAL), an island model modified to allow different active constraints in the distributed subpopulations and novel concepts of Probability of Migration and Probability of Complete Migration