A framework for isogeometric-analysis-based design and optimization of wind turbine blades

Typical wind turbine blade design procedures employ reduced-order models almost exclusively for early-stage design; high-fidelity, finite-element-based procedures are reserved for later design stages because they entail complex workflows, large volumes of data, and significant computational expense. Yet, high-fidelity structural analyses often provide design-governing feedback such as buckling load factors. Mitigation of the issues of workflow complexity, data volume, and computational expense would allow designers to utilize high-fidelity structural analysis feedback earlier, more easily, and more often in the design process. Thus, this work presents a blade analysis framework which employs isogeometric analysis (IGA), a simulation method that overcomes many of the aforementioned drawbacks associated with traditional finite element analysis (FEA). IGA directly utilizes the mathematical models generated by computer-aided design (CAD) software, requires less user interaction and no conversion of CAD geometries to finite element meshes, and tends to have superior per-degree-of-freedom accuracy compared to traditional FEA. The presented framework employs the parametric capabilities of the Grasshopper algorithmic modeling interface developed for the CAD software Rhinoceros 3D. This Grasshopper-based framework enables seamless, iterative design and IGA of CAD-based geometries and is demonstrated through the optimization of both a pressurized tube and a simplified wind turbine blade design. Further, because engineering models, such as wind turbine blades, are typically composed of numerous surface patches, a novel patch coupling technique is presented. For the sake of straightforward implementation and flexibility, the coupling technique is based on a penalty energy approach. Formulations for the penalty parameters are proposed to eliminate the problem-dependent nature of the penalty method. This coupling methodology is successfully demonstrated using a number of multi-patch benchmark examples with both matching and non-matching interface discretizations. Together, these technologies enable practical and efficient design and analysis of wind turbine blade shell structures. The presented IGA approach is employed to perform vibration, buckling, and nonlinear deformation analysis of the NREL/SNL 5 MW wind turbine blade, validating the effectiveness of the proposed approach for realistic, composite wind turbine blade designs. Further, a blade design framework that combines reduced-order aeroelastic analysis with the presented IGA methodologies is outlined. Aeroelastic analysis is used to efficiently provide dynamic kinematic data for a wide range of wind load cases, while IGA is used to perform high-fidelity buckling analysis. Finally, the value and feasibility of incorporating high-fidelity IGA feedback into optimization is demonstrated through optimization of the NREL/SNL 5 MW wind turbine blade. Alternative structural designs that have improved blade mass and material cost characteristics are identified, and IGA-based buckling analysis is shown to provide design-governing constraint information

An Improved Binary Grey-Wolf Optimizer with Simulated Annealing for Feature Selection

This paper proposes improvements to the binary grey-wolf optimizer (BGWO) to solve the feature selection (FS) problem associated with high data dimensionality, irrelevant, noisy, and redundant data that will then allow machine learning algorithms to attain better classification/clustering accuracy in less training time. We propose three variants of BGWO in addition to the standard variant, applying different transfer functions to tackle the FS problem. Because BGWO generates continuous values and FS needs discrete values, a number of V-shaped, S-shaped, and U-shaped transfer functions were investigated for incorporation with BGWO to convert their continuous values to binary. After investigation, we note that the performance of BGWO is affected by the selection of the transfer function. Then, in the first variant, we look to reduce the local minima problem by integrating an exploration capability to update the position of the grey wolf randomly within the search space with a certain probability; this variant was abbreviated as IBGWO. Consequently, a novel mutation strategy is proposed to select a number of the worst grey wolves in the population which are updated toward the best solution and randomly within the search space based on a certain probability to determine if the update is either toward the best or randomly. The number of the worst grey wolf selected by this strategy is linearly increased with the iteration. Finally, this strategy is combined with IBGWO to produce the second variant of BGWO that was abbreviated as LIBGWO. In the last variant, simulated annealing (SA) was integrated with LIBGWO to search around the best-so-far solution at the end of each iteration in order to identify better solutions. The performance of the proposed variants was validated on 32 datasets taken from the UCI repository and compared with six wrapper feature selection methods. The experiments show the superiority of the proposed improved variants in producing better classification accuracy than the other selected wrapper feature selection algorithms

Parametric freeform-based construction site layout optimization

Traditional approaches to the construction site layout problem have been focused mainly on rectilinear facilities where the importance proximity measures are mainly based on Cartesian distances between the centroids of the facilities. This is a fair abstraction of the problem; however it ignores the fact that many facilities on construction sites assume non-rectilinear shapes that allow for better compaction within tight sites. The main focus of this research is to develop a new approach of modeling site facilities to surpass limitations and inefficiencies of previous models and to ensure a more realistic approach to construction site layout problems. A construction site layout optimization model was developed that can suit both static and dynamic site layouts. The developed model is capable of modeling any rectilinear and non-rectilinear site shapes, especially splines, since it utilizes a parametric modeling software. The model also has the ability to mimic the “dynamic” behavior of the objects’ shapes through the introduction and development of three different algorithms for dynamic shapes; where the geometrical shapes representing site facilities automatically modify their geometrical forms to fit in strict areas on site. Moreover, the model provides different proximity measures and distance measurement techniques rather than the normal centroidal Cartesian distances used in most models. The new proximity measures take into consideration actual movement between the facilities including any passageways or access roads on site. Furthermore, the concept of selective zoning was introduced and a corresponding algorithm was provided; where the concept significantly enhances optimization efficiency by minimizing the number of solutions through selection of pre-determined movement zones on site. Soft constraints for buffer zones around the site facilities were developed as well. The site layout modeling was formulated on commercial parametric modeling tools (Rhino® and Grasshopper®) and the optimization was performed through genetic algorithms. After each of the algorithms was verified and validated, a case study of a real dynamic site layout planning problem was made to validate the comprehensive model combining all of the modules together. Different proximity measures and distance measurement techniques were considered, along with different static and dynamic geometrical shapes for the temporary facilities. The model produced valid near-optimum solutions, a comparison was then made between the layout that is produced with the model and the layout that would have been produced by other models to demonstrate the capabilities and advantages of the produced model

An Intelligent Hybrid Sentiment Analyzer for Personal Protective Medical Equipments Based on Word Embedding Technique: The COVID-19 Era

Due to the accelerated growth of symmetrical sentiment data across different platforms, experimenting with different sentiment analysis (SA) techniques allows for better decision-making and strategic planning for different sectors. Specifically, the emergence of COVID-19 has enriched the data of people’s opinions and feelings about medical products. In this paper, we analyze people’s sentiments about the products of a well-known e-commerce website named Alibaba.com. People’s sentiments are experimented with using a novel evolutionary approach by applying advanced pre-trained word embedding for word presentations and combining them with an evolutionary feature selection mechanism to classify these opinions into different levels of ratings. The proposed approach is based on harmony search algorithm and different classification techniques including random forest, k-nearest neighbor, AdaBoost, bagging, SVM, and REPtree to achieve competitive results with the least possible features. The experiments are conducted on five different datasets including medical gloves, hand sanitizer, medical oxygen, face masks, and a combination of all these datasets. The results show that the harmony search algorithm successfully reduced the number of features by 94.25%, 89.5%, 89.25%, 92.5%, and 84.25% for the medical glove, hand sanitizer, medical oxygen, face masks, and whole datasets, respectively, while keeping a competitive performance in terms of accuracy and root mean square error (RMSE) for the classification techniques and decreasing the computational time required for classification

A Survey on Natural Inspired Computing (NIC): Algorithms and Challenges

Nature employs interactive images to incorporate end users2019; awareness and implication aptitude form inspirations into statistical/algorithmic information investigation procedures. Nature-inspired Computing (NIC) is an energetic research exploration field that has appliances in various areas, like as optimization, computational intelligence, evolutionary computation, multi-objective optimization, data mining, resource management, robotics, transportation and vehicle routing. The promising playing field of NIC focal point on managing substantial, assorted and self-motivated dimensions of information all the way through the incorporation of individual opinion by means of inspiration as well as communication methods in the study practices. In addition, it is the permutation of correlated study parts together with Bio-inspired computing, Artificial Intelligence and Machine learning that revolves efficient diagnostics interested in a competent pasture of study. This article intend at given that a summary of Nature-inspired Computing, its capacity and concepts and particulars the most significant scientific study algorithms in the field

Comparative analysis of evolutionary-based maximum power point tracking for partial shaded photovoltaic

The characteristics of the photovoltaic module are affected by the level of solar irradiation and the ambient temperature. These characteristics are depicted in a V-P curve. In the V-P curve, a line is drawn that shows the response of changes in output power to the level of solar irradiation and the response to changes in voltage to ambient temperature. Under partial shading conditions, photovoltaic (PV) modules experience non-uniform irradiation. This causes the V-P curve to have more than one maximum power point (MPP). The MPP with the highest value is called the global MPP, while the other MPP is the local MPP. The conventional MPP tracking technique cannot overcome this partial shading condition because it will be trapped in the local MPP. This article discusses the MPP tracking technique using an evolutionary algorithm (EA). The EAs analyzed in this article are genetic algorithm (GA), firefly algorithm (FA), and fruit fly optimization (FFO). The performance of MPP tracking is shown by comparing the value of the output power, accuracy, time, and tracking effectiveness. The performance analysis for the partial shading case was carried out on various populations and generations

Hyperparameter-Optimized Machine Learning Techniques for Mammogram Classification

Computer technology has employed Machine Learning models in a variety of applications to improve performance. The hyperparameter of a machine learning model must be adapted to overcome learning limitations and increase its performance. In this research, the hyperparameters of machine learning classifiers are tuned to identify cases of benign or malignant breast abnormalities. An experimental investigation was conducted using the Wisconsin Diagnosis Breast Cancer (WDBC) Dataset. A fusion model, Bayesian Optimization Hyper Band-Naïve Bayes (BOHB-NB) is employed, which is combined with conventional classification approaches like Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM).  The proposed methods are compared to cutting-edge models like SVM, NB, LR, K-Nearest Neighbour (KNN), Random Forest, and Decision Tree using a wide range of parametric measures, such as Precision, Recall, Specificity, F-measure, Accuracy, True Positivity Rate (TPR), and False Positivity Rate (FPR). The results show that the proposed methods outperform the leading models