Educational Simulator for Teaching of Particle Swarm Optimization in LabVIEW

This paper presents an educational software tool for aid the teaching of Particle Swarm Optimization (PSO) fundamentals with friendly design interface. This software were developed in the platform of LabVIEW (Laboratory Virtual Intrumentation Engineering Workbench). The software‟s best qualities are users can select many different version of the PSO algorithm, a lot of the benchmarks test functions for optimization and set the parameters that have an influence on the PSO performance. Through visualization of particle distribution in the searching, the simulator is particularly effective in providing users with an intuitive feel for the PSO algorithm

State of the Art in the Optimisation of Wind Turbine Performance Using CFD

Wind energy has received increasing attention in recent years due to its sustainability and geographically wide availability. The efficiency of wind energy utilisation highly depends on the performance of wind turbines, which convert the kinetic energy in wind into electrical energy. In order to optimise wind turbine performance and reduce the cost of next-generation wind turbines, it is crucial to have a view of the state of the art in the key aspects on the performance optimisation of wind turbines using Computational Fluid Dynamics (CFD), which has attracted enormous interest in the development of next-generation wind turbines in recent years. This paper presents a comprehensive review of the state-of-the-art progress on optimisation of wind turbine performance using CFD, reviewing the objective functions to judge the performance of wind turbine, CFD approaches applied in the simulation of wind turbines and optimisation algorithms for wind turbine performance. This paper has been written for both researchers new to this research area by summarising underlying theory whilst presenting a comprehensive review on the up-to-date studies, and experts in the field of study by collecting a comprehensive list of related references where the details of computational methods that have been employed lately can be obtained

Meta-model-based multi-objective optimization for robust color reproduction using hybrid diffraction gratings

International audienceWe propose an efficient and versatile optimization scheme, based on the combination of multi-objective genetic algorithms and neural-networks, to reproduce specific colors through the optimization of the geometrical parameters of metal-dielectric diffraction gratings. To illustrate and assess the performance of this approach, we tailor the chromatic response of a structure composed of three adjacent hybrid V-groove diffraction gratings. To be close to the experimental situation, we include the feasibility constraints imposed by the fabrication process. The strength of our approach lies in the possibility to simultaneously optimize different contradictory objectives, avoiding time-consuming electromagnetic calculations

Web Application with Machine Learning for House Price Prediction

Every year, the price of a house changes due to different aspects, so accurately estimating the buying and selling price is a problem for real estate agencies. Therefore, the research work aims to build a Machine Learning (ML) model in Azure ML Studio and a web application to predict the buying and selling price of two types of houses: urban and rural houses, according to their characteristics, to minimize the forecast error in prediction. Following the basic stages of machine learning construction, we build the prediction model and the Rational Unified Process (RUP) methodology to build the web application. As a result, we obtained a model trained with a linear regression algorithm and a predictive ML model with a coefficient of determination of 95% and a web application that consumes the prediction model through an Application Programming Interface (API) that facilitates price prediction to customers. The quality of the prediction system was evaluated by expert judgment; they evaluated efficiency, usability, and functionality. After the calculation, they obtained an average quality of 4.88, which indicates that the quality is very high. In conclusion, the developed prediction system facilitates real estate agencies and their customers the accurate prediction of the price of urban and rural housing, minimizing accuracy errors in price prediction. Benefiting all people interested in the real estate world

Multi-disciplinary analysis and optimization Java tool for aircraft design

The first stages of the aircraft design process require to carry out multi-disciplinary analyses as fast as possible, and with a certain grade of accuracy. During the conceptual and the preliminary phases, the goal is to search for the design that best fulfils the requirements. This work presents a Java framework, named JPAD, developed at the University of Naples Federico II by the Design of Aircraft and Flight technologies research group (DAF) to perform multi-disciplinary analysis and optimization of transport aircraft. This paper describes all the JPAD capabilities, focusing on the sensitivity analyses and optimization modules. At the end, a case study concerning the optimization of a regional turboprop aircraft model similar to the well-known ATR72 will be presented

Multi-Objective structural optimization of repairs of blisk blades

Modern manufacturing technologies offer multiple options to extend the service life of expensive jet engine components through repairs. In this context, the repair processes of blade-integrated disks (blisks) are of particular interest, as the complex design makes replacement of this part very costly. However, currently, repairs of blisks are mainly done manually and repair design decisions still rely on the expertise of maintenance technicians. From a scientific perspective, these subjective, experience-based decisions are a major drawback, as today’s computational methods allow for systematic analysis and evaluation of design alternatives. The present doctoral thesis contributes to the decision-making process related to the repair of blisk blades by blending and patching by providing an engineering optimization framework and simulation routines for structural assessment of different repair designs. First, an object-oriented optimization framework is developed that is ideally suited to address engineering optimization problems such as blisk repair optimization. The design of the software architecture is chosen to achieve a high degree of flexibility and modularity. In particular, the framework provides a unified interface for global and local derivative-free optimization algorithms and custom engineering optimization problems. Thereby, optimization of single- as well as multi-objective problems is supported. The broad applicability of the framework in engineering optimization is demonstrated using examples from wind energy research. Furthermore, the optimization framework forms a suitable environment for structural multi-objective optimization of blend and patch repairs. The second part of this thesis is devoted to the application of the optimization framework to blend repairs of a compressor blisk. The geometry of the removed blade part and the resulting blend is parameterized by three geometric design variables. The two objectives of the optimization correspond to two modal criteria, because especially the vibration behavior of blades is affected by this kind of geometric modification. To check if frequency requirements are harmed by the repair the first objective reflects the deviation of the natural frequencies of the repaired blade to the natural frequencies of the nominal blade. The second objective considers resonance conditions by evaluating the proximity of natural frequencies to excitation frequencies. Pareto optimal repair designs are found by solving the derived optimization problem using appropriate structural mechanics models of a blade sector and employing the developed optimization framework. By analyzing the optimal blend shapes for two different damage patterns, it is shown that the characteristics of Pareto frontiers, like the occurrence of discontinuities, are damage-specific. Therefore, it is concluded that design decisions on blend repairs have to be made on a case-by-case basis. The third part of this thesis is concerned with the multi-objective optimization of patch repairs. While blend repairs change the blade geometry, patch repairs restore the original blade contour. In terms of structural integrity, the most significant modification due to patching is hence associated with the welding process to join patch and blade. The remaining residual stresses, affect the strength of the repaired blade, are therefore the most critical aspect of patch repairs. Utilizing the engineering optimization framework and the parametric simulation model, a multi-objective optimization problem is solved considering the length of the weld and the fatigue strength of the repaired blade. In addition to fatigue strength properties, the weld length is selected as an optimization goal, since the manufacturing effort of the high-tech repair is of practical importance. Pareto optimal repair designs are presented for a damage pattern at the leading edge. The optimization results are further complemented by subsequent thermal and mechanical simulations of the welding and heat treatment process. Different patch geometries are classified from the Pareto optimal solutions. Depending on the preferences in terms of weld length and the High-Cycle Fatigue strength of different load cases, short or long patches are to be used. In addition, the results show that some potential patch designs are not optimal in any case, and therefore can be completely excluded. Finally, the benefits of the unified interface of the engineering optimization framework are emphasized. Different optimization settings of a patch repair optimization are presented and compared utilizing the hypervolume metric. Concluding remarks on the potential of computational methods for improved repair design and an outlook on future maintenance of blisks complete this work.DFG/SFB 871/119 193 472./E

A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-core CPUs

Swarm intelligence algorithms (SIAs) have demonstrated excellent performance when solving optimization problems including many real-world problems. However, because of their expensive computational cost for some complex problems, SIAs need to be accelerated effectively for better performance. This paper presents a high-performance general framework to accelerate SIAs (FASI). Different from the previous work which accelerate SIAs through enhancing the parallelization only, FASI considers both the memory architectures of hardware platforms and the dataflow of SIAs, and it reschedules the framework of SIAs as a converged dataflow to improve the memory access efficiency. FASI achieves higher acceleration ability by matching the algorithm framework to the hardware architectures. We also design deep optimized structures of the parallelization and convergence of FASI based on the characteristics of specific hardware platforms. We take the quantum behaved particle swarm optimization algorithm (QPSO) as a case to evaluate FASI. The results show that FASI improves the throughput of SIAs and provides better performance through optimizing the hardware implementations. In our experiments, FASI achieves a maximum of 290.7Mbit/s throughput which is higher than several existing systems, and FASI on FPGAs achieves a better speedup than that on GPUs and multi-core CPUs. FASI is up to 123 times and not less than 1.45 times faster in terms of optimization time on Xilinx Kintex Ultrascale xcku040 when compares to Intel Core i7-6700 CPU/ NVIDIA GTX1080 GPU. Finally, we compare the differences of deploying FASI on hardware platforms and provide some guidelines for promoting the acceleration performance according to the hardware architectures

Multi-objective particle swarm optimization for the structural design of concentric tube continuum robots for medical applications

Concentric tube robots belong to the class of continuum robotic systems whose morphology is described by continuous tangent curvature vectors. They are composed of multiple, interacting tubes nested inside one another and are characterized by their inherent flexibility. Concentric tube continuum robots equipped with tools at their distal end have high potential in minimally invasive surgery. Their morphology enables them to reach sites within the body that are inaccessible with commercial tools or that require large incisions. Further, they can be deployed through a tight lumen or follow a nonlinear path. Fundamental research has been the focus during the last years bringing them closer to the operating room. However, there remain challenges that require attention. The structural synthesis of concentric tube continuum robots is one of these challenges, as these types of robots are characterized by their large parameter space. On the one hand, this is advantageous, as they can be deployed in different patients, anatomies, or medical applications. On the other hand, the composition of the tubes and their design is not a straightforward task but one that requires intensive knowledge of anatomy and structural behavior. Prior to the utilization of such robots, the composition of tubes (i.e. the selection of design parameters and application-specific constraints) must be solved to determine a robotic design that is specifically targeted towards an application or patient. Kinematic models that describe the change in morphology and complex motion increase the complexity of this synthesis, as their mathematical description is highly nonlinear. Thus, the state of the art is concerned with the structural design of these types of robots and proposes optimization algorithms to solve for a composition of tubes for a specific patient case or application. However, existing approaches do not consider the overall parameter space, cannot handle the nonlinearity of the model, or multiple objectives that describe most medical applications and tasks. This work aims to solve these fundamental challenges by solving the parameter optimization problem by utilizing a multi-objective optimization algorithm. The main concern of this thesis is the general methodology to solve for patient- and application-specific design of concentric tube continuum robots and presents key parameters, objectives, and constraints. The proposed optimization method is based on evolutionary concepts that can handle multiple objectives, where the set of parameters is represented by a decision vector that can be of variable dimension in multidimensional space. Global optimization algorithms specifically target the constrained search space of concentric tube continuum robots and nonlinear optimization enables to handle the highly nonlinear elasticity modeling. The proposed methodology is then evaluated based on three examples that include cooperative task deployment of two robotic arms, structural stiffness optimization under the consideration of workspace constraints and external forces, and laser-induced thermal therapy in the brain using a concentric tube continuum robot. In summary, the main contributions are 1) the development of an optimization methodology that describes the key parameters, objectives, and constraints of the parameter optimization problem of concentric tube continuum robots, 2) the selection of an appropriate optimization algorithm that can handle the multidimensional search space and diversity of the optimization problem with multiple objectives, and 3) the evaluation of the proposed optimization methodology and structural synthesis based on three real applications