Computational approach for form-finding optimal design

In this paper, an optimization strategy for a canopy, based on computational modelling approaches is presented. The design approach is applied to a realistic roof structure of an ecological island (waste collection centre) and has been completely redesigned with the aid of a Genetic Algorithm and a Dynamic Relaxation Algorithm. The preliminary design of the roof structure can be formulated as a shape optimization problem, involving functional needs and constraints at different scales of the structure. The proposed hypothesis solution was studied by using an optimization procedure through algorithms in the software Rhinoceros3D®/Grasshopper®. The main aim of this work is to explore different modelling approaches for form-finding that can be built from the use of numerical simulations based on algorithms. To this aim, the need to meet various requirements (structural, functional, formal) involving a team of architects and engineers can be interpreted as a matter of structural optimizatio

Parametric geometrical modelling of wind turbine Blades & Hub.

Wind turbine industry has substantially grown in last decades and is still becoming larger. Hence, effective ways to design wind turbines are needed. In this report, the procedure of parametric design of a wind turbine blade is presented. This is performed with the help of Grasshopper®, an application embedded into Rhinoceros 3D graphical design software. The process involves parametric construction of airfoil sections along the wind turbine blade. The geometry is modeled by taking advantage of Bezier curves, since they can be used to build nonlinear high curvature contours. Main feature curves in an airfoil are the camber, upper and lower sides, which are defined by a set of appropriately selected parameters. The generation of multiple airfoil sections enables us to construct a complete 3D shape with help of the lofting feature in Rhinoceros environment. Results can be further utilized for static or dynamic analysis. The aim of the project is to develop an algorithm that enables us to reconstruct any available wind turbine blade with maximum precision and to establish a verified solution method by reproducing published data for NACA 4412 in the subsonic flow regime. The blade design is described in detail in this report and all the stages are presented with appropriate illustrations

메타 휴리스틱 최적화를 이용한 기계학습 기반 선박 건조 공정 리드 타임 예측

학위논문(석사) -- 서울대학교대학원 : 공과대학 조선해양공학과, 2021.8. 하오유주.In the shipbuilding industry, each production process has a respective lead time; that is, the duration between start and finish times. Lead time is basic data that is necessary for high-efficiency production planning and systematic production management. Therefore, lead time must be accurate. However, the traditional method of lead time management is not scientific because it mostly makes the plan by calculating the average lead times derived from historical data. Therefore, to understand the complex relationship between lead time and other influencing factors, this study proposes to use machine learning (ML) algorithms, support vector machine (SVM) and artificial neural network (ANN), which are frequently applied in prediction fields. Moreover, to improve prediction accuracy, this study proposes to apply meta-heuristic algorithms to optimize the parameters of the ML models. This thesis builds hybrid models, including meta-heuristic-ANN, meta-heuristic-SVM models. In addition, this study compares model’s performance with each other. In searching for the ML model’s parameters, the results point out that the new self-organizing hierarchical particle swarm optimization (PSO) with jumping time-varying acceleration coefficients (NHPSO-JTVAC) algorithm is superior in terms of performance. More importantly, the test results demonstrate that the integrated models, based on NHPSO-JTVAC, have the smallest mean absolute percentage error (MAPE) test error in the three shipyard block process data sets, 11.79%, 16.03% and 16.45%, respectively. The results also demonstrate that the built models based on NHPSO-JTVAC can achieve further meaningful enhancements in terms of prediction accuracy. Overall, the NHPSO–JTVAC-SVM, NHPSO–JTVAC-ANN models are feasible for predicting the lead time in shipbuilding.조선 산업에서 각 공정은 리드 타임을 가진다. 리드 타임이란 공정 시작과 종료 간에 시간으로, 고효율의 생산계획과 체계적 생산관리를 위해 매우 중요한 지표이다. 특히, 생산 계획 단계에서 정확한 리드타임 예측은 납기 준수를 위한 계획 수립을 위해 매우 중요하다. 그러나 기존의 예측 방법은 과거 데이터의 평균값을 사용했기 때문에 정확도가 매우 떨어졌다. 따라서 본 연구에서는 리드 타임과 다른 영향 요인 간의 복잡한 관계를 이해하기 위해 예측 분야에서 자주 적용되는 머신 러닝 (ML) 모델인 서포트 벡터 머신 (SVM) 및 인공 신경망 (ANN) 적용을 제안한다. 또한, 기계학습 모델 예측 정확도를 향상시키기 위해 메타 휴리스틱 알고리즘을 적용하여 모델의 파라미터를 최적화하고자 한다. 본 연구는 meta-heuristics-ANN, meta-heuristics-SVM 모델을 포함하는 하이브리드 모델을 구축한다. 더불어, 본 연구는 메타 휴리스틱 알고리즘 기반으로 최적화된 기계학습 모델의 성능을 서로 비교한다. 연구 결과를 통해, ML 모델의 파라미터를 탐색하는 과정에서 particle swam optimization (PSO)의 enhanced 버전인 NHPSO-JTVAC 알고리즘이 탐색 성능 면에서 다른 알고리즘보다 우수하다는 것을 알 수 있다. 뿐만 아니라 테스트 결과를 살펴보면 NHPSO-JTVAC에 기반한 하이브리드 모델이 조선소 세 개의 블록 공정 데이터에서 (각각 11.79%, 16.03% 및 16.45%) 가장 작은 MAPE 테스트 오차임을 알 수 있다. 이것은 NHPSO-JTVAC를 기반으로 구축된 모델이 예측 정확도 측면에서 의미 있는 향상을 더 달성할 수 있음을 보여준다. 전반적으로 NHPSO-JTVAC-SVM, NHPSO-JTVAC-ANN 모델은 조선소 블록 공정의 리드 타임을 예측하는 데 적합하다는 것을 확인할 수 있다.Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Related Works 3 1.2.1 Related Works for Lead Time Prediction 3 1.2.2 Related Works for Hybrid Predictive Model 4 1.3 Thesis Organization 6 Chapter 2 Machine Learning 7 2.1 Support Vector Machine 7 2.1.1 Support Vector Machine Algorithm 7 2.1.2 Hyperparameter Optimization for SVM 10 2.2 Artificial Neural Network 11 2.2.1 Artificial Neural Network Algorithm 11 2.2.2 Hyperparameter Optimization for ANN 15 Chapter 3 Meta-heuristic Optimization Algorithms 17 3.1 Particle Swarm Optimization 17 3.2 NHPSO-JTVAC: An Advanced Version of PSO 18 3.3 Bat Algorithm 19 3.4 Firefly Algorithm 21 3.5 Grasshopper Optimization Algorithm 22 3.6 Moth Search Algorithm 24 Chapter 4 Hybrid Artificial Intelligence Models 27 4.1 Hybrid Meta-heuristic-SVM Models 27 4.1.1 Hybrid PSO-SVM Model 29 4.1.2 Hybrid NHPSO-JTVAC-SVM Model 30 4.1.3 Hybrid BA-SVM Model 31 4.1.4 Hybrid FA-SVM Model 33 4.1.5 Hybrid GOA-SVM Model 34 4.1.6 Hybrid MSA-SVM Model 35 4.2 Hybrid Meta-heuristic-ANN Models 36 4.2.1 Hybrid PSO-ANN Model 38 4.2.2 Hybrid NHPSO-JTVAC-ANN Model 39 4.2.3 Hybrid BA-ANN Model 40 4.2.4 Hybrid FA-ANN Model 41 4.2.5 Hybrid GOA-ANN Model 42 4.2.6 Hybrid MSA-ANN Model 43 Chapter 5 Lead Time Prediction Based on Hybrid AI Models 44 5.1 Data and Preparation 44 5.1.1 Data Normalization 45 5.1.2 Feature Selection 45 5.2 Lead Time Prediction 46 5.3 Performance Metrics 47 Chapter 6 Experimental Results 49 6.1 Results Based on Hybrid SVM-based Models 49 6.2 Results Based on Hybrid ANN-based Models 55 6.3 Overall Results 60 Chapter 7 Conclusions and Future Works 62 Bibliography 63 Appendix A 68 Abstract in Korean 69석

Survey on Various Aspects of Clustering in Wireless Sensor Networks Employing Classical, Optimization, and Machine Learning Techniques

A wide range of academic scholars, engineers, scientific and technology communities are interested in energy utilization of Wireless Sensor Networks (WSNs). Their extensive research is going on in areas like scalability, coverage, energy efficiency, data communication, connection, load balancing, security, reliability and network lifespan. Individual researchers are searching for affordable methods to enhance the solutions to existing problems that show unique techniques, protocols, concepts, and algorithms in the wanted domain. Review studies typically offer complete, simple access or a solution to these problems. Taking into account this motivating factor and the effect of clustering on the decline of energy, this article focuses on clustering techniques using various wireless sensor networks aspects. The important contribution of this paper is to give a succinct overview of clustering

Force-Driven Weave Patterns for Shell Structures in Architectural Design

The use of lightweight carbon fiber reinforced polymers (CFRP) in the discipline of architecture opens new possibilities for the construction of architectural components. CFRP has been explored mainly in engineering fields, such as aeronautics, automotive, ballistic and marine engineering. CFRP has also been explored in the discipline of architecture in the construction of shell structures because of its high strength-to-weight ratio and low-cost. There is, however, limited research on how structural analysis can be used to inform weave patterns for shell structures using CFRP. Further, previous research in the field has not performed physical structural tests to validate which force driven weave patterns perform best. This thesis addresses this gap by contributing a methodology for the creation of CFRP weave patterns from structural analysis and their validation through physical testing. Specifically, this thesis addresses three main problems: Firstly, understanding and analyzing the structural behavior of a shell structure through computation; Secondly, the creation of a weaving pattern of carbon fiber optimized for structural performance; the third part seeks to translate the digital model into fabricated prototypes. The results of this research show that force-flow derived patterns perform best. Consequently, force-flow is the information we should implement to create a more efficient force-driven weave pattern in shell structures. Adviser: David Newto

Algorithm-aided Information Design: Hybrid Design approach on the edge of associative methodologies in AEC

Dissertação de mestrado em European Master in Building Information ModellingLast three decades have brought colossal progress to design methodologies within the common pursuit toward a seamless fusion between digital and physical worlds and augmenting it with the of computation power and network coverage. For this historically short period, two generations of methodologies and tools have emerged: Additive generation and parametric Associative generation of CAD. Currently, designers worldwide engaged in new forms of design exploration. From this race, two prominent methodologies have developed from Associative Design approach – Object-Oriented Design (OOD) and Algorithm-Aided Design (AAD). The primary research objective is to investigate, examine, and push boundaries between OOD and AAD for new design space determination, where advantages of both design methods are fused to produce a new generation methodology which is called in the present study AID (Algorithm-aided Information Design). The study methodology is structured into two flows. In the first flow, existing CAD methodologies are investigated, and the conceptual framework is extracted based on the state of art analysis, then analysed data is synthesized into the subject proposal. In the second flow, tools and workflows are elaborated and examined on practice to confirm the subject proposal. In compliance, the content of the research consists of two theoretical and practical parts. In the first theoretical part, a literature review is conducted, and assumptions are made to speculate about AID methodology, its tools, possible advantages and drawbacks. Next, case studies are performed according to sequential stages of digital design through the lens of practical AID methodology implementation. Case studies are covering such design aspects as model & documentation generation, design automation, interoperability, manufacturing control, performance analysis and optimization. Ultimately, a set of test projects is developed with the AID methodology applied. After the practical part, research returns to the theory where analytical information is gathered based on the literature review, conceptual framework, and experimental practice reports. In summary, the study synthesizes AID methodology as part of Hybrid Design, which enables creative use of tools and elaborating of agile design systems integrating additive and associative methodologies of Digital Design. In general, the study is based on agile methods and cyclic research development mixed between practice and theory to achieve a comprehensive vision of the subject.Last three decades have brought colossal progress to design methodologies within the common pursuit toward a seamless fusion between digital and physical worlds and augmenting it with the of computation power and network coverage. For this historically short period, two generations of methodologies and tools have emerged: Additive generation and parametric Associative generation of CAD. Currently, designers worldwide engaged in new forms of design exploration. From this race, two prominent methodologies have developed from Associative Design approach – Object-Oriented Design (OOD) and Algorithm-Aided Design (AAD). The primary research objective is to investigate, examine, and push boundaries between OOD and AAD for new design space determination, where advantages of both design methods are fused to produce a new generation methodology which is called in the present study AID (Algorithm-aided Information Design). The study methodology is structured into two flows. In the first flow, existing CAD methodologies are investigated, and the conceptual framework is extracted based on the state of art analysis, then analysed data is synthesized into the subject proposal. In the second flow, tools and workflows are elaborated and examined on practice to confirm the subject proposal. In compliance, the content of the research consists of two theoretical and practical parts. In the first theoretical part, a literature review is conducted, and assumptions are made to speculate about AID methodology, its tools, possible advantages and drawbacks. Next, case studies are performed according to sequential stages of digital design through the lens of practical AID methodology implementation. Case studies are covering such design aspects as model & documentation generation, design automation, interoperability, manufacturing control, performance analysis and optimization. Ultimately, a set of test projects is developed with the AID methodology applied. After the practical part, research returns to the theory where analytical information is gathered based on the literature review, conceptual framework, and experimental practice reports. In summary, the study synthesizes AID methodology as part of Hybrid Design, which enables creative use of tools and elaborating of agile design systems integrating additive and associative methodologies of Digital Design. In general, the study is based on agile methods and cyclic research development mixed between practice and theory to achieve a comprehensive vision of the subject