Author: Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 768
Book Description
Scientific and Technical Aerospace Reports
Author: International Aerospace Abstracts
Author: Chemical Abstracts
Author: Granulation
Author: Agba D. SalmanPublisher: Elsevier
ISBN: 0080467881
Category : Science
Languages : en
Pages : 1403
Book Description
Granulation provides a complete and comprehensive introduction on the state-of-the-art of granulation and how it can be applied both in an academic context and from an industrial perspective. Coupling science and engineering practices it covers differing length scales from the sub-granule level through behaviour through single granules, to bulk granule behaviour and equipment design. With special focus on a wide range of industrially relevant areas from fertilizer production, through to pharmaceuticals. Experimental data is complemented by mathematical modelling in this emerging field, allowing for a greater understanding of the basis of particle products and this important industry sector.Four themes run through the book: 1. The Macro Scale processing for Granulation – including up to date descriptions of the methods used for granulation and how they come about and how to monitor – on-line these changes.2. The Applications of granulation from an industrial perspective, with current descriptive roles and how they are undertaken with relevance to industry, and effective properties.3. Mechanistic descriptions of granulation and the different rate processes occurring within the granulator. This includes methods of modelling the process using Population – Balance Equations, and Multi-level Computational Fluid Dynamics Models.4. The Micro Scale: Granules and Smaller, looking at single granules and there interactions and modelling, while also considering the structure of granules and their constituent liquid bridges.* Covers a wide range of subjects and industrial applications* Provides an understanding of current issues for industrial and academic environments* Allows the reader an understanding of the science behind engineered granulation processes
Japanese Science and Technology
Author: Decision-making in a Fuzzy Environment
Author: Richard BellmanPublisher:
ISBN:
Category : Fuzzy sets
Languages : en
Pages : 76
Book Description
Government Reports Announcements & Index
Author: Materials Discovery and Design
Author: Turab LookmanPublisher: Springer
ISBN: 3319994654
Category : Science
Languages : en
Pages : 266
Book Description
This book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role of inference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key to achieving the desired goals of real time analysis and feedback. Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes of in situ spatially and temporally resolved data per sample. The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader.
Uncertainty Quantification in Laminated Composites
Author: Sudip DeyPublisher: CRC Press
ISBN: 1351651641
Category : Mathematics
Languages : en
Pages : 307
Book Description
Over the last few decades, uncertainty quantification in composite materials and structures has gained a lot of attention from the research community as a result of industrial requirements. This book presents computationally efficient uncertainty quantification schemes following meta-model-based approaches for stochasticity in material and geometric parameters of laminated composite structures. Several metamodels have been studied and comparative results have been presented for different static and dynamic responses. Results for sensitivity analyses are provided for a comprehensive coverage of the relative importance of different material and geometric parameters in the global structural responses.
Machine Learning
Author: Kevin P. MurphyPublisher: MIT Press
ISBN: 0262018020
Category : Computers
Languages : en
Pages : 1102
Book Description
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.