클라우드 컴퓨팅 환경기반에서 수치 모델링과 머신러닝을 통한 지구과학 자료생성에 관한 연구

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 지구환경과학부, 2022. 8. 조양기.To investigate changes and phenomena on Earth, many scientists use high-resolution-model results based on numerical models or develop and utilize machine learning-based prediction models with observed data. As information technology advances, there is a need for a practical methodology for generating local and global high-resolution numerical modeling and machine learning-based earth science data. This study recommends data generation and processing using high-resolution numerical models of earth science and machine learning-based prediction models in a cloud environment. To verify the reproducibility and portability of high-resolution numerical ocean model implementation on cloud computing, I simulated and analyzed the performance of a numerical ocean model at various resolutions in the model domain, including the Northwest Pacific Ocean, the East Sea, and the Yellow Sea. With the containerization method, it was possible to respond to changes in various infrastructure environments and achieve computational reproducibility effectively. The data augmentation of subsurface temperature data was performed using generative models to prepare large datasets for model training to predict the vertical temperature distribution in the ocean. To train the prediction model, data augmentation was performed using a generative model for observed data that is relatively insufficient compared to satellite dataset. In addition to observation data, HYCOM datasets were used for performance comparison, and the data distribution of augmented data was similar to the input data distribution. The ensemble method, which combines stand-alone predictive models, improved the performance of the predictive model compared to that of the model based on the existing observed data. Large amounts of computational resources were required for data synthesis, and the synthesis was performed in a cloud-based graphics processing unit environment. High-resolution numerical ocean model simulation, predictive model development, and the data generation method can improve predictive capabilities in the field of ocean science. The numerical modeling and generative models based on cloud computing used in this study can be broadly applied to various fields of earth science.지구의 변화와 현상을 연구하기 위해 많은 과학자들은 수치 모델을 기반으로 한 고해상도 모델 결과를 사용하거나 관측된 데이터로 머신러닝 기반 예측 모델을 개발하고 활용한다. 정보기술이 발전함에 따라 지역 및 전 지구적인 고해상도 수치 모델링과 머신러닝 기반 지구과학 데이터 생성을 위한 실용적인 방법론이 필요하다. 본 연구는 지구과학의 고해상도 수치 모델과 머신러닝 기반 예측 모델을 기반으로 한 데이터 생성 및 처리가 클라우드 환경에서 효과적으로 구현될 수 있음을 제안한다. 클라우드 컴퓨팅에서 고해상도 수치 해양 모델 구현의 재현성과 이식성을 검증하기 위해 북서태평양, 동해, 황해 등 모델 영역의 다양한 해상도에서 수치 해양 모델의 성능을 시뮬레이션하고 분석하였다. 컨테이너화 방식을 통해 다양한 인프라 환경 변화에 대응하고 계산 재현성을 효과적으로 확보할 수 있었다. 머신러닝 기반 데이터 생성의 적용을 검증하기 위해 생성 모델을 이용한 표층 이하 온도 데이터의 데이터 증강을 실행하여 해양의 수직 온도 분포를 예측하는 모델 훈련을 위한 대용량 데이터 세트를 준비했다. 예측모델 훈련을 위해 위성 데이터에 비해 상대적으로 부족한 관측 데이터에 대해서 생성 모델을 사용하여 데이터 증강을 수행하였다. 모델의 예측성능 비교에는 관측 데이터 외에도 HYCOM 데이터 세트를 사용하였으며, 증강 데이터의 데이터 분포는 입력 데이터 분포와 유사함을 확인하였다. 독립형 예측 모델을 결합한 앙상블 방식은 기존 관측 데이터를 기반으로 하는 예측 모델의 성능에 비해 향상되었다. 데이터합성을 위해 많은 양의 계산 자원이 필요했으며, 데이터 합성은 클라우드 기반 GPU 환경에서 수행되었다. 고해상도 수치 해양 모델 시뮬레이션, 예측 모델 개발, 데이터 생성 방법은 해양 과학 분야에서 예측 능력을 향상시킬 수 있다. 본 연구에서 사용된 클라우드 컴퓨팅 기반의 수치 모델링 및 생성 모델은 지구 과학의 다양한 분야에 광범위하게 적용될 수 있다.1. General Introduction 1 2. Performance of numerical ocean modeling on cloud computing 6 2.1. Introduction 6 2.2. Cloud Computing 9 2.2.1. Cloud computing overview 9 2.2.2. Commercial cloud computing services 12 2.3. Numerical model for performance analysis of commercial clouds 15 2.3.1. High Performance Linpack Benchmark 15 2.3.2. Benchmark Sustainable Memory Bandwidth and Memory Latency 16 2.3.3. Numerical Ocean Model 16 2.3.4. Deployment of Numerical Ocean Model and Benchmark Packages on Cloud Clusters 19 2.4. Simulation results 21 2.4.1. Benchmark simulation 21 2.4.2. Ocean model simulation 24 2.5. Analysis of ROMS performance on commercial clouds 26 2.5.1. Performance of ROMS according to H/W resources 26 2.5.2. Performance of ROMS according to grid size 34 2.6. Summary 41 3. Reproducibility of numerical ocean model on the cloud computing 44 3.1. Introduction 44 3.2. Containerization of numerical ocean model 47 3.2.1. Container virtualization 47 3.2.2. Container-based architecture for HPC 49 3.2.3. Container-based architecture for hybrid cloud 53 3.3. Materials and Methods 55 3.3.1. Comparison of traditional and container based HPC cluster workflows 55 3.3.2. Model domain and datasets for numerical simulation 57 3.3.3. Building the container image and registration in the repository 59 3.3.4. Configuring a numeric model execution cluster 64 3.4. Results and Discussion 74 3.4.1. Reproducibility 74 3.4.2. Portability and Performance 76 3.5. Conclusions 81 4. Generative models for the prediction of ocean temperature profile 84 4.1. Introduction 84 4.2. Materials and Methods 87 4.2.1. Model domain and datasets for predicting the subsurface temperature 87 4.2.2. Model architecture for predicting the subsurface temperature 90 4.2.3. Neural network generative models 91 4.2.4. Prediction Models 97 4.2.5. Accuracy 103 4.3. Results and Discussion 104 4.3.1. Data Generation 104 4.3.2. Ensemble Prediction 109 4.3.3. Limitations of this study and future works 111 4.4. Conclusion 111 5. Summary and conclusion 114 6. References 118 7. Abstract (in Korean) 140박

Comparative analysis of spring flood risk reduction measures in Alaska, United States and the Sakha Republic, Russia

Thesis (Ph.D.) University of Alaska Fairbanks, 2017River ice thaw and breakup are an annual springtime phenomena in the North. Depending on regional weather patterns and river morphology, breakups can result in catastrophic floods in exposed and vulnerable communities. Breakup flood risk is especially high in rural and remote northern communities, where flood relief and recovery are complicated by unique geographical and climatological features, and limited physical and communication infrastructure. Proactive spring flood management would significantly minimize the adverse impacts of spring floods. Proactive flood management entails flood risk reduction through advances in ice jam and flood prevention, forecasting and mitigation, and community preparedness. With the goal to identify best practices in spring flood risk reduction, I conducted a comparative case study between two flood-prone communities, Galena in Alaska, United States and Edeytsy in the Sakha Republic, Russia. Within a week from each other, Galena and Edeytsy sustained major floods in May 2013. Methods included focus groups with the representatives from flood managing agencies, surveys of families impacted by the 2013 floods, observations on site, and archival review. Comparative parameters of the study included natural and human causes of spring floods, effectiveness of spring flood mitigation and preparedness strategies, and the role of interagency communication and cooperation in flood risk reduction. The analysis revealed that spring flood risk in Galena and Edeytsy results from complex interactions among a series of natural processes and human actions that generate conditions of hazard, exposure, and vulnerability. Therefore, flood risk in Galena and Edeytsy can be reduced by managing conditions of ice-jam floods, and decreasing exposure and vulnerability of the at-risk populations. Implementing the Pressure and Release model to analyze the vulnerability progression of Edeytsy and Galena points to common root causes at the two research sites, including colonial heritage, unequal distribution of resources and power, top-down governance, and limited inclusion of local communities in the decision-making process. To construct an appropriate flood risk reduction framework it is important to establish a dialogue among the diverse stakeholders on potential solutions, arriving at a range of top-down and bottom-up initiatives and in conjunction selecting the appropriate strategies. Both communities have progressed in terms of greater awareness of the hazard, reduction in vulnerabilities, and a shift to more reliance on shelter-in-place. However, in neither community have needed improvements in levee protection been completed. Dialogue between outside authorities and the community begins earlier and is more intensive for Edeytsy, perhaps accounting for Edeytsy's more favorable rating of risk management and response than Galena's

Reduced order methods for laminar and turbulent flows in a finite volume setting: projection-based methods and data-driven techniques

This dissertation presents a family of Reduced Order Models (ROMs) which is specifically designed to deal with both laminar and turbulent flows in a finite volume full order setting. Several aspects associated with the reduction of the incompressible Navier\u2013Stokes equations have been investigated. The first of them is related to the need of an accurate reduced pressure reconstruction. This issue has been studied with the help of two main approaches which consist in the use of the Pressure Poisson Equation (PPE) at the reduced order level and also the employment of the supremizer stabilization method. A second aspect is connected with the enforcement of non-homogeneous Dirichlet boundary conditions at the inlet boundary at the reduced order level. The solutions to address this aspect include two methods, namely, the lifting function method and the penalty method. Different solutions for the treatment of turbulence at the reduced order level have been proposed. We have developed a unified reduction approach which is capable of dealing with turbulent flows based on the Reynolds Averaged Navier\u2013Stokes (RANS) equations complemented by any Eddy Viscosity Model (EVM). The turbulent ROM developed is versatile in the sense that it may be applied on the FOM solutions obtained by different turbulent closure models or EVMs. This is made possible thanks to the formulation of the ROM which merges projection-based techniques with data-driven reduction strategies. In particular, the work presents a mixed strategy that exploits a data-driven reduction method to approximate the eddy viscosity solution manifold and a classical POD-Galerkin projection approach for the velocity and the pressure fields. The newly proposed turbulent ROM has been validated on benchmark test cases in both steady and unsteady settings with Reynolds up to Re 10 to 5

Interdisciplinarity in science education in France: analysis of approaches and their application to the vibrating string case study

This thesis explores the relations among STEM-disciplines in France, with a special focus on the interaction between mathematics and physics. It presents the French high school system that recently underwent a governmental reform called BAC 2021. The work examines different approaches to interdisciplinarity in a context of science education, illustrating in particular the Julie Thompson Klein’s taxonomy. Six French researchers working with interdisciplinary were interviewed. The analysis with the Klein’s theoretical lenses of the definitions of interdisciplinarity collected in the interviews points out some practical criteria to compare and distinguish among methodological, theoretical, instrumental and critical interdisciplinarity. The thesis claims that the collaboration of people having different backgrounds is enriching because experts of various domains know different epistemologies. Some critical opinion towards the new reform BAC 2021 are expressed, especially about interdisciplinarity. Despite the introduction of a new subject of science education, it has emerged the fact that in the French education system the STEM topics seem still not to be present enough. Introducing the STEM-related problems in the teaching should be supported by an adequate university teaching research. In the end, a historical case study of the vibrating string controversy is presented. Starting with a modern treatment of the problem, the thesis problematizes the fact that often it is presented as a simple exercise, whilst, it represented a very emblematic case of interdisciplinarity. Through the historical contextualisation with the biographies of the protagonists and the analysis of the debate it highlights the analysis of the interdisciplinarity between mathematics and physics in a theme proposed in the second-grade syllabus of the school subject “science education”. The last part of the chapter develops the narrative of the debate and suggests some didactic implementations

Computational micro-flow with spectral element method and high Reynolds number flow with discontinuous Galerkin Finite Element Method

In this dissertation, two numerical methods with high order accuracy, Spectral Element Method (SEM) and Discontinuous Galerkin Finite Element Method (DG-FEM), are chosen to solve problems in Computational Fluid Dynamics (CFD). The merits of these two methods will be discussed and utilized in different kinds of CFD problems. The simulations of the micro-flow systems with complex geometries and physical applications will be presented by SEM. Moreover, the numerical solutions for the Hyperbolic Flow will be obtained by DG-FEM. By solving problems with these two methods, the differences between them will be discussed as well. Compressible Navier-Stokes equations with Electro-osmosis body force and slip boundary conditions are solved to simulate two independent models. The third order Adams-Bashforth method on time splitting, and up to the eighth order SEM on space analysis are utilized in our cases of the electro-osmosis flow (EOF). To solve the body force caused by EOF, simplification of the Poisson-Boltzmann is discussed in details. Results show that SEM can clearly simulate the electric double layers in EOF. Compared with the finite element method, which uses h-refinement to increase resolution, SEM has obvious advantages by using hp-refinement. The other case for SEM is the simulations of drug delivery through the micro needle. The drug flowing inside the needle is treated as a micro-flow system with complex geometry, while the process of drug fluxing in human skin is developed as in the case of CFD problem in porous media. Incompressible Navier-Stokes equations and Darcy-Brinkman equations are solved to simulate the drug flowing inside the needle and diffusing in human skin, respectively. Results are compared with COMSOL simulation, experimental data, and numerical solutions from Smoothed Particle Hydrodynamics (SPH). The high order DG-FEM method is chosen to do research on Hyperbolic Flow