scholarly journals Aerodynamic Design Optimization of a Morphing Leading Edge and Trailing Edge Airfoil–Application on the UAS-S45

2021 ◽  
Vol 11 (4) ◽  
pp. 1664
Author(s):  
Musavir Bashir ◽  
Simon Longtin-Martel ◽  
Ruxandra Mihaela Botez ◽  
Tony Wong
Keyword(s):  
Search Algorithm ◽  
Optimization Technique ◽  
Leading Edge ◽  
Endurance Performance ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Pattern Search ◽  
Aerodynamic Optimization ◽  
Trailing Edge ◽  
Sst Turbulence Model

This work presents an aerodynamic optimization method for a Droop Nose Leading Edge (DNLE) and Morphing Trailing Edge (MTE) of a UAS-S45 root airfoil by using Bezier-PARSEC parameterization. The method is performed using a hybrid optimization technique based on a Particle Swarm Optimization (PSO) algorithm combined with a Pattern Search algorithm. This is needed to provide an efficient exploitation of the potential configurations obtained by the PSO algorithm. The drag minimization and the endurance maximization were investigated for these configurations individually as two single-objective optimization functions. The aerodynamic calculations in the optimization framework were performed using the XFOIL solver with flow transition estimation criteria, and these results were next validated with a Computational Fluid Dynamics solver using the Transition γ−Reθ Shear Stress Transport (SST) turbulence model. The optimization was conducted at different flight conditions. Both the DNLE and MTE optimized airfoils showed a significant improvement in the overall aerodynamic performance, and MTE airfoils increased the efficiency of CL3/2/CD by 10.25%, indicating better endurance performance. Therefore, both DNLE and MTE configurations show promising results in enhancing the aerodynamic efficiency of the UAS-S45 airfoil.

Solid Mechanics Based Design and Optimization for Support Structure Generation in Stereolithography Based Additive Manufacturing

2015 ◽  
Author(s):  
Guanglei Zhao ◽  
Chi Zhou ◽  
Sonjoy Das
Keyword(s):  
Additive Manufacturing ◽  
Solid Mechanics ◽  
Search Algorithm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Optimized Design ◽  
Support Structures ◽  
Support Structure ◽  
Element Analysis ◽  
Structure Generation

Support structures are typically required to hold parts in place in various additive manufacturing processes. Design of support structure includes identifying both anchor locations and geometries. Extensive work has been done to optimize the anchor locations to reliably keep part in position, and minimize the contacting area as well as the total volume of the support structures. However, relatively few studies have been focused on the mechanical property analysis of the structure. In this paper, we proposed a novel design optimization method to identify the anchor geometry based on solid mechanics theory. Finite element analysis method is utilized to study the stress distribution on both the support structure and main part. Particle Swarm Optimization (PSO) algorithm with a novel constraining handling strategy is employed to optimize the design model. A gradient descent local search algorithm is utilized to quickly locate the global solution in the vicinity explored by PSO. The developed optimization framework is deployed on a bottom-up projection based Stereolithography process. The experimental results show that the optimized design can efficiently reduce the material used on support structure and marks left on the part.

scholarly journals Weight Minimization of Helical Compression Spring Using Gravitational Search Algorithm (GSA)

2015 ◽  
Vol 773-774 ◽  
pp. 277-281 ◽  
Author(s):  
Noor Hafizah Amer ◽  
Nurhidayati Ahmad ◽  
Amar Faiz Zainal Abidin
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Standard Case ◽  
Weight Minimization ◽  
Compression Spring ◽  
Parametric Studies ◽  
Gravitational Search ◽  
Better Than

Compression spring is one of the most common mechanical componet being used in most mechanisms. Many criteria and constraints should be considered in designing and specifying the spring dimensions. Therefore, it has been one of the standard case studies considered to test a new optimisation algorithm. This paper introduced an optimization method named Gravitational search Algorithm (GSA) to solve the problem of weight minimization of spring. From previous studies, weight minimization of a spring has been investigated by many researcher using various optimization algorithm technique. The result of this study were compared to one of the previous studies using Particle Swarm Optimization (PSO) algorithm. Also, parametric studies were conducted to select the best values of GSA parameters, beta and epsilon. From the results obtained, it was observed that the optimum dimensions and weight obtained by GSA are better than the values obtained by PSO. The best values of beta and epsilon was found to be 0.6 and 0.01 respectively.

Generation of Optimal Artificial Neural Networks Using a Pattern Search Algorithm: Application to Approximation of Chemical Systems

2008 ◽  
Vol 20 (2) ◽  
pp. 573-601 ◽  
Author(s):  
Matthias Ihme ◽  
Alison L. Marsden ◽  
Heinz Pitsch
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Source Term ◽  
Transfer Functions ◽  
Search Algorithm ◽  
Optimization Method ◽  
Pattern Search ◽  
Memory Storage ◽  
Categorical Variables ◽  
Artificial Neural

A pattern search optimization method is applied to the generation of optimal artificial neural networks (ANNs). Optimization is performed using a mixed variable extension to the generalized pattern search method. This method offers the advantage that categorical variables, such as neural transfer functions and nodal connectivities, can be used as parameters in optimization. When used together with a surrogate, the resulting algorithm is highly efficient for expensive objective functions. Results demonstrate the effectiveness of this method in optimizing an ANN for the number of neurons, the type of transfer function, and the connectivity among neurons. The optimization method is applied to a chemistry approximation of practical relevance. In this application, temperature and a chemical source term are approximated as functions of two independent parameters using optimal ANNs. Comparison of the performance of optimal ANNs with conventional tabulation methods demonstrates equivalent accuracy by considerable savings in memory storage. The architecture of the optimal ANN for the approximation of the chemical source term consists of a fully connected feedforward network having four nonlinear hidden layers and 117 synaptic weights. An equivalent representation of the chemical source term using tabulation techniques would require a 500 × 500 grid point discretization of the parameter space.

scholarly journals A Multiobjective Optimal Operation of a Stand-Alone Microgrid Using SAPSO Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Guoping Zhang ◽  
Weijun Wang ◽  
Jie Du ◽  
Hua Liu
Keyword(s):  
Optimization Model ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Optimal Operation ◽  
Renewable Energy Resources ◽  
Diesel Generator ◽  
Pareto Solution ◽  
Operation Optimization

Microgrid is an effective way to utilize renewable energy resources, especially for satisfying the electricity requirements in remote islands. The operation optimization of an island microgrid is critical to ensure the effective performance of the whole microgrid system, and it is usually a multiconstrained and multiobjective optimization problem. The main contribution of this study is an operation optimization method for the stand-alone microgrid system in a remote island, which includes wind, PV, battery, and diesel generator. In this paper, a novel operation optimization model for stand-alone microgrid is proposed, in which the battery system is considered separately; the multiobjective day-ahead optimization model considering economic cost, battery depreciation cost, and environmental protection cost is established. In the optimization, the output power of diesel generator and energy storage system are chosen as the decision variables. For this purpose, an efficient search algorithm combining the particle swarm optimization (PSO) algorithm and the simulated annealing (SA) algorithm is developed. The hybrid algorithm is applied to search for the Pareto solution set of the optimization problem. The search results are compared with those from traditional PSO algorithm. Also, a grey target decision-making theory based on the entropy weight method is proposed to identify the best trade-off scheduling scheme among all the solutions, and the results are compared with those from two other commonly used subjective and objective methods. The results show that the proposed optimization method can be applied to the day-ahead operation optimization of the microgrid system and help the user obtain the best compromise operation scheme for stand-alone microgrid.

scholarly journals An Optimized PV Control System Based on the Emperor Penguin Optimizer

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 751
Author(s):  
Mariam A. Sameh ◽  
Mostafa I. Marei ◽  
M. A. Badr ◽  
Mahmoud A. Attia
Keyword(s):  
Duty Cycle ◽  
Optimization Technique ◽  
Pso Algorithm ◽  
Boost Converter ◽  
Emperor Penguin ◽  
Pv System ◽  
Partial Shading ◽  
Pv Systems ◽  
Point Tracking ◽  
Grid Connected Inverter

During the day, photovoltaic (PV) systems are exposed to different sunlight conditions in addition to partial shading (PS). Accordingly, maximum power point tracking (MPPT) techniques have become essential for PV systems to secure harvesting the maximum possible power from the PV modules. In this paper, optimized control is performed through the application of relatively newly developed optimization algorithms to PV systems under Partial Shading (PS) conditions. The initial value of the duty cycle of the boost converter is optimized for maximizing the amount of power extracted from the PV arrays. The emperor penguin optimizer (EPO) is proposed not only to optimize the initial setting of duty cycle but to tune the gains of controllers used for the boost converter and the grid-connected inverter of the PV system. In addition, the performance of the proposed system based on the EPO algorithm is compared with another newly developed optimization technique based on the cuttlefish algorithm (CFA). Moreover, particle swarm optimization (PSO) algorithm is used as a reference algorithm to compare results with both EPO and CFA. PSO is chosen since it is an old, well-tested, and effective algorithm. For the evaluation of performance of the proposed PV system using the proposed algorithms under different PS conditions, results are recorded and introduced.

scholarly journals Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4649
Author(s):  
İsmail Hakkı ÇAVDAR ◽  
Vahit FERYAD
Keyword(s):  
Smart Grids ◽  
Energy Demand ◽  
Optimization Technique ◽  
Cost Effective ◽  
Optimization Method ◽  
Long Term Memory ◽  
Successful Implementation ◽  
Efficient Design ◽  
Energy Disaggregation ◽  
Load Monitoring

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

scholarly journals Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

2021 ◽  
Vol 13 (3) ◽  
pp. 1274
Author(s):  
Loau Al-Bahrani ◽  
Mehdi Seyedmahmoudian ◽  
Ben Horan ◽  
Alex Stojcevski
Keyword(s):  
Large Scale ◽  
Thermal Power ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Pso Algorithm ◽  
Search Space ◽  
Economic Benefits ◽  
Optimization Techniques ◽  
Ramp Rate ◽  
Dynamic Economic Dispatch

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

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