Yu Wang – författare

Development and Application of Classical Capillary Curve Theory shows readers how to improve chemical flooding recovery procedures in enhanced oil recovery (EOR) using sophisticated numerical modeling techniques for oil displacement that build on, and enhance, the accepted capillary curve in a novel way. The title presents research and technical data that amends the classical capillary curve, giving new impetus to chemical flooding research. By analyzing the capillary number experiment QL, the book models oil and water distribution in the micro-space of the reservoir core.

Finally, the book discusses chemical flooding software, reservoir geological modeling, field tests, and a new technique of digital oil displacement testing being developed.

This book starts with the basic ideas in uncertainty propagation using Monte Carlo methods and the generation of random variables and stochastic processes for some common distributions encountered in engineering applications. It then introduces a class of powerful simulation techniques called Markov Chain Monte Carlo method (MCMC), an important machinery behind Subset Simulation that allows one to generate samples for investigating rare scenarios in a probabilistically consistent manner. The theory of Subset Simulation is then presented, addressing related practical issues encountered in the actual implementation. The book also introduces the reader to probabilistic failure analysis and reliability-based sensitivity analysis, which are laid out in a context that can be efficiently tackled with Subset Simulation or Monte Carlo simulation in general. The book is supplemented with an Excel VBA code that provides a user-friendly tool for the reader to gain hands-on experience with Monte Carlo simulation.

This book starts with the basic ideas in uncertainty propagation using Monte Carlo methods and the generation of random variables and stochastic processes for some common distributions encountered in engineering applications. It then introduces a class of powerful simulation techniques called Markov Chain Monte Carlo method (MCMC), an important machinery behind Subset Simulation that allows one to generate samples for investigating rare scenarios in a probabilistically consistent manner. The theory of Subset Simulation is then presented, addressing related practical issues encountered in the actual implementation. The book also introduces the reader to probabilistic failure analysis and reliability-based sensitivity analysis, which are laid out in a context that can be efficiently tackled with Subset Simulation or Monte Carlo simulation in general. The book is supplemented with an Excel VBA code that provides a user-friendly tool for the reader to gain hands-on experience with Monte Carlo simulation.

The 13 full papers presented in this volume were reviewed and selected from 16 submissions. The papers cover a broad spectrum of applications of reconfigurable computing, from driving assistance, data and graph processing acceleration, computer security to the societal relevant topic of supporting early diagnosis of Covid infectious conditions.

Presenting an optimal energy distribution strategy for microgrids in a smart grid environment, and featuring a detailed analysis of the mathematical techniques of convex optimization and online algorithms, this book provides readers with essential content on how to achieve multi-objective optimization that takes into consideration power subscribers, energy providers and grid smoothing in microgrids. Featuring detailed theoretical proofs and simulation results that demonstrate and evaluate the correctness and effectiveness of the algorithm, this text explains step-by-step how the problem can be reformulated and solved, and how to achieve the distributed online algorithm on the basis of a centralized offline algorithm. Special attention is paid to how to apply this algorithm in practical cases and the possible future trends of the microgrid and smart grid research and applications. Offering a valuable guide to help researchers and students better understand the new smart grid, this book will also familiarize readers with the concept of the microgrid and its relationship with renewable energy.

This book constitutes the proceedings of the Second International Conference on Big Data Computing and Communications, BigCom 2016, held in Shenyang, China, in July 2016. The 39 papers presented in this volume were carefully reviewed and selected from 90 submissions.

BigCom is an international symposium dedicated to addressing the challenges emerging from big data related computing and networking.

The conference is targeted to attract researchers and practitioners who are interested in Big Data analytics, management, security and privacy, communication and high performance computing in its broadest sense.

This book describes algorithmic methods and parallelization techniques to design a parallel sparse direct solver which is specifically targeted at integrated circuit simulation problems. The authors describe a complete flow and detailed parallel algorithms of the sparse direct solver. They also show how to improve the performance by simple but effective numerical techniques. The sparse direct solver techniques described can be applied to any SPICE-like integrated circuit simulator and have been proven to be high-performance in actual circuit simulation. Readers will benefit from the state-of-the-art parallel integrated circuit simulation techniques described in this book, especially the latest parallel sparse matrix solution techniques.